Next Article in Journal
Joint Particle Swarm Optimization of Power and Phase Shift for IRS-Aided D2D Underlaying Cellular Systems
Next Article in Special Issue
Transparent Pneumatic Tactile Sensors for Soft Biomedical Robotics
Previous Article in Journal
Computational Study of a Motion Sensor to Simultaneously Measure Two Physical Quantities in All Three Directions for a UAV
Previous Article in Special Issue
Near-Infrared Light-Responsive Hydrogels for Highly Flexible Bionic Photosensors
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

A Brief Review on Flexible Electronics for IoT: Solutions for Sustainability and New Perspectives for Designers

Department of Engineering, University of Messina, 98166 Messina, Italy
*
Author to whom correspondence should be addressed.
Sensors 2023, 23(11), 5264; https://doi.org/10.3390/s23115264
Submission received: 1 May 2023 / Revised: 26 May 2023 / Accepted: 31 May 2023 / Published: 1 June 2023
(This article belongs to the Special Issue The Advanced Flexible Electronic Devices)

Abstract

:
The Internet of Things (IoT) is gaining more and more popularity and it is establishing itself in all areas, from industry to everyday life. Given its pervasiveness and considering the problems that afflict today’s world, that must be carefully monitored and addressed to guarantee a future for the new generations, the sustainability of technological solutions must be a focal point in the activities of researchers in the field. Many of these solutions are based on flexible, printed or wearable electronics. The choice of materials therefore becomes fundamental, just as it is crucial to provide the necessary power supply in a green way. In this paper we want to analyze the state of the art of flexible electronics for the IoT, paying particular attention to the issue of sustainability. Furthermore, considerations will be made on how the skills required for the designers of such flexible circuits, the features required to the new design tools and the characterization of electronic circuits are changing.

1. Introduction

The term sustainability has now become commonly used, it is of great importance and is also used in different contexts. It was used for the first time in 1992, during the first UN Conference on the environment. The definition of sustainability that has been given is this: Condition of a development model capable of ensuring the satisfaction of the needs of the present generation without compromising the possibility of future generations to realize their own [1]. This definition is centered not only on the economy and society, but above all on ecology. Sustainability and sustainable development are linked to a new idea of well-being that takes into account people’s quality of life. Environmental sustainability requires responsibility in the use of resources. It is therefore a development model to which everyone can and must contribute, starting from the awareness that every action performed by each of us has a deep impact on the environment.
Based on these considerations, the world of electronics, which for decades has been increasingly pervasive in all sectors of life (industry, medical, automation, automotive, military, consumption), cannot fail to pay maximum attention to the issue of sustainability. The electronics as fuel of the Internet of Things technology is surely leading us in a new way of conducting our lives and cities [2], also allowing the optimization of the production processes of companies and industries and the management of services and infrastructures, limiting the consumption of resources and pollution. Management of public lighting [3,4,5,6,7], air quality [8,9,10,11,12,13,14,15,16,17,18,19,20,21,22] and noise pollution monitoring [23,24,25,26,27,28,29], smart home [30,31,32,33,34,35,36,37,38,39,40,41,42,43], smart roads, smart cars, urban mobility and transport [44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63], food and agriculture [64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83], smart factories [84,85,86,87,88,89,90,91] and medicine [92,93,94,95,96,97,98,99,100,101,102] are examples of the great potentialities of the IoT. However, the increase in connectivity inevitably translates into an increase in electronic devices and systems (sensors, data acquisition and processing systems, communication systems) and therefore the problem of respecting the environment, both in the production step and disposal of disused systems is, nowadays, of fundamental importance also in the field of the IoT industry. Thanks to the availability of eco-compatible materials, flexible electronics, which is a solution that is increasingly gaining space in many applications due to its portability, wearability and low cost, could be the right path towards an increasingly green IoT (Figure 1).
Although there are many works in the scientific literature on IoT or on flexible implementations of IoT technology, there is still no overview focused exclusively on solutions that are contextually flexible and green. The most considered green aspects so far for the IoT are those aimed at energy saving [103]. In Figure 2 the collocation of this work in the available literature is explained.
To better understand, let’s consider, as they are represented in Figure 3, the main layers of an IoT architecture [104]. The perception layer (or devices layer) consists of several devices—sensors, cameras, actuators, memories, RFID that sense, acquire, store, display data and perform tasks. The network layer transmits data from devices to an on-premises or cloud data center. The processing layer (or middleware layer), which typically leverages many connected computers simultaneously, performs cloud computing, storage, networking, and security performance. The application layer decodes and compiles data in forms that are easy for users to understand, such as graphs and tables. Programs for device control and monitoring, as well as process control software, are typical examples of the application layer of IoT architecture. The more complex IoT architectures have three further layers, not shown in the figure, i.e., edge, business and security.
In this review we describe the prospects of the key technologies of flexible electronics, such as RFIDs, sensors, memories and energy harvesters, that are the basis of the perception layer of an IoT architecture, highlighting the solutions that make it possible to achieve the goal of sustainability. First, in the next section, we will illustrate solutions to make the substrates of various devices in a sustainable way, because the choice of a green substrate is a key factor that is common to all devices. In conclusion, a discussion on how the skills of an electronic circuit designer, the features of the simulation and design tools and the characterization of produced devices must change, will be performed.

2. Green Substrates: Paper and “Nanopaper”

The choice of the substrate on which to make a flexible device is surely a key factor for sustainable IoT because the greater quantity of material that makes up the device is precisely the substrate [105].
With the advent of flexible electronics, the favored substrates on which to build devices have, for a long time, been plastic materials. However, discarded plastics degrade to form micro and nano-plastics that are hazardous to human beings and the environment. If one thinks of the implementation of flexible devices that are “green”, surely paper is the first material that comes to mind as a substrate to substitute plastic [106]. In fact, paper is widely and easily available, low-cost, recyclable and biodegradable. Table 1 shows a comparison between paper and the plastic materials mostly used as substrates, in terms of impacts on climate change and resource use [107]. In this regard, it may be useful to recall that studies conducted on these same indicators as regards the production of silicon, the fundamental semiconductor in the electronics industry, have highlighted a development in the wrong direction for the silicon industry, facing increasing climate related pressures [108].
Although it is very promising from an environmental point of view and several devices made on paper substrates have appeared in the last decade, the use of paper as a substrate is still limited, due to the high surface roughness and poor barrier properties against water and solvents [106]. However, if we consider that in applications in the IoT field, and therefore in electronics, one of the main properties of the substrates is that of allowing optimization of the device performance in terms of conductivity, paper substrates have performances no lower than the plastic ones most used up to now. In [106] an interesting comparison is made between different paper substrates and PET substrates. The main results are summarized in Table 2.
Continuing with the comparison between paper and plastic substrates, wishing to evaluate the performances in terms of elasticity, in Table 3 we report Young’s modulus. Among the plastic materials we have considered PET, precisely because it is the most used, PEN (polyethylene naphthalate) which has performances in terms of elasticity superior to other plastic substrates, and PDMS (polydimethylsiloxane), a popular elastomer in the manufacture of stretchable devices [109]. Results in Table 3 show that paper substrates can offer elastic performances comparable to PDMS under proper coating conditions.
Obviously, if the goal of making flexible devices that are absolutely sustainable is to be achieved, the separation of electronic materials, conductive metallic inks in most cases, from the paper substrate at the end of life of the devices must be easily performed. To overcome these limitations, a few solutions based on coating approaches have been presented to improve paper substrate performances. As an example, the use of shellac, that is a cheap biopolymer, has been proposed in [110]. Shellac, employed as a coating surface for paper substrates, forms planarized, printable surfaces. At the end of the life of the device, shellac behaves as a sacrificial layer that can be removed by immersing the printed device in methanol, enabling the separation of the paper substrate. Nevertheless, coating procedures and other surface treatments are not effective for all electronics applications [111,112]. In the last period, “nanopaper”, that is, planar substrates made of cellulose nanomaterials (CNM), gained relevance [113,114,115,116,117,118,119,120]. CNM are nanosized particles with highly ordered cellulose chains aligned along the bundle axis, that exhibit interesting characteristics with respect to pulp fibers and wood particles, such as high mechanical properties, low thermal expansion, low density, and simplicity of treatment that allows the implementation of additional functionalities [121,122,123]. To focus on sustainability, it is also important to evaluate the end-of-life performance, that is to carry out a study on the biodegradability of materials. In [114], for example, a comparison between the biodegradation of CNM samples with respect to microcrystalline cellulose (MCC), and a commercial thermoplastic polyurethane (TPU) has been performed and the results are summarized in Table 4.
The results reported in [114] show that in the first 70 days of testing, the biodegradability rate of the CNF-HEC compounds is comparable to that of pure cellulose, while subsequently there is a slowdown. Although there is no doubt that the biodegradability of cellulose-based samples is far superior to that of plastic materials, it is certainly clear that, to further improve the state of the art, studies need to be conducted to understand how to optimize the performance of paper substrates without lowering the biodegradability performance too much compared to pure cellulose. The biodegradability of the printed substrate is slightly lower than that of the non-printed substrate, also highlighting the importance of working on the eco-sustainability of the conductive layers. Without any doubt, the “nanopaper” technology, that is a relatively low-cost technology [124,125,126] for substrate fabrication for IoT applications, is strategic to fuel a transition toward a sustainable and green IoT, also working on the use of optimized nanocellulose with other materials and hybrid structures [127,128,129,130,131].

3. Perception Layer Devices

The main perception layer devices that will be considered in the following, due to their large diffusion in IoT networks, are RFIDs, sensors, memories, and energy harvesting devices.

3.1. RFIDs

Connectivity to anything, the main characteristic of IoT, requires the unique addressability of things. The RFID (radio frequency identification) tag is considered a key technology for both addressing physical objects and sensing [132]. The original task of RFID is identification and tracking [133,134,135,136,137,138,139,140,141,142].
Technological progress has made it possible to integrate sensors into the tag, thus significantly increasing and expanding the performance of RFIDs, making them ideal for use in the IoT and in sensor networks [143,144,145,146,147,148,149,150,151]. The increase of IoT and sensor networks nodes leads to a rapid and drastic growth of the number of RFID tags produced, and IDTechEx predicts that in 2023 31.841 billion passive RFID tags will be sold, which will become 41.490 billion in 2024 and 102.330 billion in 2029 [152]. RFIDs therefore constitute an important source of e-waste, and it is necessary to make their production and disposal sustainable [133,153]. Many efforts have been made, achieving excellent results, to produce eco-sustainable RFIDs, exploiting recyclable substrates (as discussed in Section 2) and eco-compatible materials for printing the antennas and for the realization of the adhesive layer [154,155,156,157,158,159,160,161,162,163,164,165]. The commitment to make the RFID technology sustainable is visible not only in the scientific literature but also in the activities of the manufacturing companies [166,167,168]. In Figure 4 a schematic representation of a green RFID is shown. With respect to conventional RFID, a green RFID has no plastic substrate and presents fewer adhesive layers. The production process does not involve the use of toxic or environmentally harmful chemicals. Table 5 reports the results obtained with eco-friendly RFID. Although the results are quite encouraging and may allow the implementation of efficient IoT nodes, a few challenges still need to be addressed. First of all, paper substrates are surely the optimum choice in terms of eco-sustainability, but their uneven surfaces and liquid absorption must be taken into consideration and possible coating processing must be evaluated and used, to improve their functionality. Secondly, conductive material for the antenna implementation should be chosen based on conductivity, mechanical deformability, and ease of adaptation to current manufacturing methods. The metal-based conductive inks are up to now the best solution, because of their good mechanical and electrical properties and printability. According to manufacturing processes, this is another fundamental issue to be addressed. The conventional etching approach for RFID tag production consumes an excess of metal materials, and requires a series of complex processing steps, resulting in a too large amount of waste material, whereas the printing technology, being an additive manufacturing process, limits the waste of conductive material and can be accomplished in few steps and at a relatively low cost. The appropriate printing method is an essential requisite for obtaining printed flexible RFID antenna patterns with good performance.
To date, several types of printing technologies have been adopted in flexible antenna manufacturing, but lately screen printing, inkjet printing, flexographic printing, aerosol jet (AJ) and electrohydrodynamic jet (EHD) printing are preferred for their characteristics [168]. In Table 6 the key parameters of these printing technologies are reported.
Inkjet printing, which consists of transferring ink materials directly to the flexible substrates without any masks, is still the main technology to fabricate RFID, because of its simplicity. Metal solution and nano-based conductive inks are the most suitable materials for inkjet printing, because of their relatively low viscosity. The drawback of inkjet printing is that inaccuracies can affect the quality of the antenna.
Screen printing produces a pattern by forcing the ink through a screen with a fine mesh, that is divided into graphic and non-graphical areas, defining the printed pattern. This technique is also simple to implement and accurate.
Flexo-printing is the fastest printing technology and is therefore considered when the production of very large amounts of printed antennas is required.
AJ and EHD are the most recently developed techniques. In aerosol jet printing the ink is aerosolized and delivered to the substrate by a carrier gas to design the patterns. With respect to inkjet printing, aerosol jet printing provides a higher (up to four times) resolution, fewer strict requirements for the ink viscosity, but cannot print inks with low-boiling point solvents.
EHD printing generates very fine ink droplets by applying an electric field between the nozzle and the substrate; therefore, it is a promising candidate for high-resolution printing.

3.2. Sensors

Undoubtedly, in all IoT systems, sensors, which are the interface with the real world, are crucial and therefore they are the main and most diffused devices of the perception layer. It is consequently essential to be able to produce green sensors and a few review papers in the scientific literature are available on this theme [169,170,171,172,173]. What emerges from these and other works is that, in order to move towards the production of green sensors, it is necessary to work on four fronts, as summarized in Figure 5: (1) the use of eco-friendly materials as substrates [174,175]; (2) as sensing layers [176,177,178,179,180,181,182]; (3) as a coating or encapsulation [175,183]; (4) the implementation of sustainable fabrication processes [184,185,186,187,188]. As far as eco-friendly substrates are concerned, those based on paper or cellulose are the most suitable. However, the strong water absorption of paper limits its use in a few applications, for example in wearable strain sensors. In these cases, a sizing agent layer that imparts hydrophobic properties to the substrate is necessary [174]. According to the sensing layers, biomaterials that feature biocompatibility, biodegradability and bioabsorbability are key solutions. As far as paper-based sensors are concerned, we refer readers to two very in-depth reviews on the subject [189,190]. In [189] Singh et al. present different types of paper that are employed in paper-based sensors, their detection mechanism and common fabrication techniques. In [190] Tai et al. illustrate the state-of-the-art of the paper-based gas, humidity, and strain sensors, offering a comparison among their characteristics and performances. For researchers interested, in particular, in the state-of-the-art of paper-based humidity sensors we recommend reading [191,192] which is a review divided into two parts that covers all types of paper-based humidity sensors, such as capacitive, resistive, impedance, fiber-optic, mass-sensitive, microwave, and RFID. Although these reviews show that considerable progress has been made on paper-based sensors, making them an interesting perspective for the sustainable future of the IoT (and beyond), they also highlight that it is undoubtedly true that some aspects still need to be explored and improved. Indeed, the manufacturing processes, the optimization of the surface and the choice of active materials must be optimized. In this regard, carbon-based materials (such as CNTs, graphite, graphene, reduced graphene oxide, graphene oxide) are currently the main sensing active layers for fabricating paper-based sensors. Considering the importance of graphene in sensor fabrication, it is worth mentioning and highlighting laser-derived graphene (LDG) technology, which is gaining attention as a promising material for the development of new electrochemical sensors and biosensors [187]. Compared to standard and well-established methods for graphene synthesis, LDG provides many advantages in terms of performance, such as fast electron mobility, good electrical conductivity, porosity, mechanical stability, and a large surface area; moreover, LDG is cost-effective and, more importantly from the point of view of environmental sustainability, it is produced by a green synthesis. To complete the overview of carbon-based sensing materials, it is also worth mentioning daily carbon ink (DCI) containing carbon black nanoparticles that, having very good performance in terms of conductivity, dispersion, adhesion, and low cost, besides a mature industrial preparation technology due to his history, could represent an excellent alternative to other materials, especially for low-cost applications. A detailed report on DCI can be found in [193]. With the advent of innovative materials, the sensing performance of the paper-based sensors is expected to further improve. However, especially with reference to novel 2D materials, challenges in the construction of high-quality 2D films on paper surfaces that are rough and porous, still need to be addressed.
Other materials that may offer environmental benefits have been explored for realizing flexible sensors for the IoT. In Table 7 we report a few examples of sensors that are interesting from an ecological point of view, indicating, where available, information on stability after bending stress. The authors of the various works reported here declare that, within the bending tests, only a slight change in the responses of the sensors was observed.
As can be seen from Table 7, starch has been largely used as a sensing layer for flexible sensing [177,182], and [182] is an extensive review on starch applications; in [178] a sensor based on biodegradable flexible polylactic acid piezoelectric film which achieves significant longitudinal compressive and transverse tensile sensitivities and thus can act either as a pressure sensor or as a tensile sensor is described; a humidity sensor made of a thin film of electrically conductive protein nanowires is presented in [180]. In [176] an interesting solution for implementing an all-organic and all-paper pressure sensor is investigated. The use of polypyrrole (PPy) as a polymer for electrodes is one of the most popular choices in the scientific literature [206], the electrode is realized with high-conductivity PPy printing paper and the active sensing layer is implemented with low-conductivity PPy tissue paper. The structures realized with the technique reported in [176] are cuttable and foldable, therefore hollow and 3D all-paper sensors can be realized through the fabrication of kirigami or origami, granting a 3D perception capability to the sensors. The advantages of 3D structured sensors are also exploited in [183], where a skin sensor realized as a sandwich structure involving a 3D conductive network between two encapsulation layers is characterized. The sensor is biocompatible and biodegradable and encapsulated by a nontoxic water-soluble polymer. In fact, polyaniline is the active conductive filler for the 3D conductive network and silk fibroin and poly (lactic-co-glycolic acid) were used to form a network for carrying conductive materials. The 3D conductive network was encapsulated by K-carrageenan. The work presented in [202] is a novel demonstration of the combination of natural polymer (chitosan) and synthetic polymer (PVP) for next-generation semiconductor device applications.
As far as substrates are concerned, what emerged from the examined works is summarized in Table 8. From the point of view of sustainability, organic materials represent the optimal choice. Among inorganic materials, carbon materials represent a good solution. However, some magnetic materials and metals (provided they are processed as thin foils) can also offer good alternatives.
As for the materials for the sensing layer, in addition to the carbon-based materials we mentioned earlier, natural bio-origin materials possess very good features such as tailorable chemical composition as well as mechanical properties. Obviously, they are particularly attractive for sustainability, as they exhibit excellent biological characteristics such as abundant supply, biodegradability, biocompatibility, and anti-microbial activity. However, if compared to the conventional materials for electronics devices, their electrical performance is still much lower. In fact, we have found in the literature several solutions that, to overcome this limitation, involve the mixing of natural bio-origin materials with conductive materials, enabling an eco-friendly matrix for protection of the conductive components. The formed bio-composites have been shown to possess both environmental friendliness as well as high conductivity, which broadens their applications for fabricating flexible electronic devices.
Although the choice of materials is of fundamental importance to produce eco-sustainable sensors, the same importance must also be given to manufacturing techniques. With reference to this issue, in [184] the design of a green approach for synthesizing conductive polymers is discussed; in [185] a facile strategy to fabricate a compressible carbonized cellulose fiber network strengthened with in situ-synthesized polydopamine for flexible pressure sensing applications is demonstrated; in [186] a notable strategy for manufacturing a sensitive polydimethylsiloxane-derived wearable piezoresistive sensor, based on silver nanoparticles and multi-walled carbon nanotube nanocomposite films is presented that is low-cost, environmentally friendly, scalable, and industrially available.

3.3. Memories

With the increasing number of nodes in IoT networks and the need for the collection of data becoming more and more common, storage devices are gaining relevance and their implementation as flexible memories is strategic in many applications [207,208,209,210,211]. Flexible resistive random access memories (RRAM) show high potential for green nonvolatile memories implementation [212]. In Figure 6 the structure of a flexible RRAM is shown, and both the substrate and the storage layer should be implemented with eco-friendly materials.
In [213] an Al/gelatin/Ag sandwiched structure on a bio-cellulose (BC) film was demonstrated, whose texture was flexible, ductile, and could be adapted to uneven surfaces. The gelatin dielectric layer and the BC substrate were non-toxic and environmentally friendly and, moreover, the BC film could be degraded completely in soil in only 5 days, thus allowing the realization of a fully biodegradable device. Biocompatible materials are used for flexible memories, such as pectin [214], that has emerged as a suitable alternative to toxic metal oxides for resistive switching applications; carbon dot-polyvinyl pyrrolidone nanocomposite and a silver nanowire (Ag NW) network buried in a flexible gelatin film [215]; starch [177,216]; poly(ethylene furanoate) (PEF), a 100% biobased polyester, as substrate, and the biopolymer deoxyribonucleic acid (DNA) as active layer [217]; iron (Fe) ions in gelatin matrixes (gelatin composites) prepared on commercially available flexible paper substrates through the solution method [218]. In [211] a poly(3,4-ethyl enedioxythiophene):polystyrene sulfonate (PEDOT:PSS)/ZnO/PEDOT:PSS transparent printed memory structure was presented that was fully fabricated using a sinter-free inkjet based process. The process conditions used in this work had the advantage of making zinc oxide non-toxic. The material selection for storage devices is particularly crucial for biomedical applications. Silk fibroin as a dielectric layer to fabricate biodegradable RRAM proved to be a good solution [219]. In [219] a W/Silk fibroin/Mg sandwich structure was studied, that provided a stable bipolar resistive switching behavior with good repeatability, surpassing the performance of most organic resistive memory and was comparable to inorganic resistive memory. Furthermore, the solubility test in phosphate buffered saline indicates the device exhibited good biodegradability. In [220] the natural biomaterial egg protein as the active layer for a RRAM was employed, and the designed device exhibited a write-once-read-many memory property.
In Table 9 the representative examples of RRAM are summarized, specifying the materials from which they were made, the performances in terms of on/off current ratio, operation voltage and data retention time, and the main benefits in terms of sustainability.
All the works reported in Table 9 declare negligible variations of the on/off ratio as a function of the bending angle and the number of cycles of bending.

3.4. Energy Harvesters

Considering that billions of devices will compose the IoT infrastructure in the near future, the issue of providing power supply is considered as crucial. The possibility of making each device self-powered is certainly attractive, and the energy harvesting technique is becoming more and more popular. In fact, by exploiting the energy supplied by the environment in various forms (mechanical, thermal, solar, radio frequency, wind) it is possible to replace batteries, with considerable simplification of flexible systems and, above all, with great benefits for the environment [221]. As with the other perception layer devices, the realization on eco-sustainable substrates is also essential for energy harvesting devices [222,223,224]. In Figure 7 a summary of the different types of environmental energy with the relative devices used to harvest it are shown.
The piezoelectric and triboelectric effects allow for mechanical energy harvesting, turning lost mechanical energy into valuable electrical energy [225]. Piezoelectric nanogenerators can convert the small vibrations of the environment, human body motions, etc., into useful electrical energy [226,227,228,229]; triboelectric nanogenerators provide higher output and are more cost effective [230,231,232,233]; but the combination of piezoelectric and triboelectric effects is a highly rated choice, in order to improve the output performance of a single nanogenerator, allowing the extraction of more electricity from a single device [234,235,236,237,238].
Recently, also thermoelectric (TE) energy harvesting technology using polymer-based TE materials has gained more and more attention [239]. Among the various polymers used in TE materials, cellulose plays a crucial role with a view to create devices that are not only flexible, but ecological [240]. Although organic thermoelectric materials have exhibited good performance, their thermoelectric efficiency is still too low to be commercially applied and produced and the interactions among the electric conductivity, Seebeck coefficient, and thermal conductivity still need to be optimized [241].
In the scientific literature there are also examples of energy harvesting that optimize performance by combining the recycling of energy from different sources. As examples, in [242] the integration of an RF energy harvester and a thermal energy harvester is presented, capable of collecting ambient energy 24 h a day; in [243] a flexible and wearable hybrid radio frequency and solar energy harvesting system for powering wearable electronic devices is discussed. In Table 10 representative examples of energy harvesting devices are reported, specifying the materials from which they are made, the type of energy they harvest, and the main benefits in terms of sustainability. All the listed solutions have proven to be a stepping-stone towards achieving self-powered, environment-friendly Internet of Things networks.

4. Discussion: The Perspective of the Designer

4.1. Designer’s Skills and Competencies

The need to pursue the objective of sustainability which, given the vast pervasiveness of electronic devices, requires the use of alternative materials to conventional ones (for example silicon) and power supply techniques based on energy harvesting, will inevitably change the preparation required for electronic engineers and the typical performance of simulation and design tools. To be able to design an electronic device such as those presented in the previous sections, the designer must possess not only knowledge of electronics (and device physics) but must also have a good preparation in the field of materials science in order to be able to make the correct choice of substrates and dielectric and conductive materials considering the applications and required working conditions. When dealing with flexible electronics, knowledge of mechanics and technical physics are mandatory to predict or analyze the effects of strains on the performance of devices or circuits. Additionally, if eco-sustainability is one of the project’s objectives, an in-depth knowledge of the production processes and disposal processes at the end of the devices’ life is also required. Although it is always possible to carry out a project in collaboration with researchers from different fields, to facilitate the interaction between different skills is still important to broaden one’s knowledge. The skills required in an electronic green flexible device designer are summarized in Figure 8. In addition, simulation and design tools must also integrate electronics, materials science and mechanical facilities. Therefore, the user who uses such design software must be able to handle it comprehensively.

4.2. Required Features of Design Tools

Simulation tools have always been fundamental in the design of electronic circuits, and they are also fundamental to the latest generation of flexible devices. Clearly, the simulators used up until now have limitations, as they are based on models derived from conventional semiconductor theory. The design of biodegradable, eco-friendly flexible electronic devices present many challenges for electronics system simulators and CAD (computer aided design) software. For example, new tools must take into account the properties of new materials, both those used as substrates and those used as dielectrics or conductors. Their interaction with electromagnetic fields must be foreseen, by simulating the shielding capability of new polymers and biomaterials against electromagnetic interferences. Any variations in impedance, capacitive or inductive couplings, or drift of the characteristics must be considered not only as a function of the environmental working conditions, but in the presence of embedding in other materials or tissues (even humans or animals). Effects of mechanical stress (bending, stretching or twisting) on structures and electrical performances must be estimated. Given the diffusion of self-powered systems by energy harvesting techniques, simulation tools that take into account the conversion efficiency would also be useful. Based on all these considerations, a convergence between mechanical design and electronic design is desirable. In Figure 9 a summary of the new features that are required for modern simulation and CAD software is shown.
However, at present we are still far from having a simulator of this type. Progress has been made in the simulation of new materials (e.g., organic) applied to electronic devices from a physical point of view. For example, SCAPS-1D, a simulation tool for thin film solar cells developed at ELIS, University of Gent, [244] is used to better understand the physics of perovskite solar cells to optimize the devices’ efficiencies [245,246] also in conjunction with machine learning techniques [247].
Since many researchers and designers in the field of electronics usually work with SPICE-like simulators, studies aimed at importing device models implemented with new materials into SPICE are of particular importance. In [248] a compact DC model of organic thin film transistors (OTFTs) and its SPICE implementation is presented and the experimental data on the fabricated devices resulted in good agreement with SPICE simulation results; in [249] a SPICE compatible compact modeling of IGZO (Indium gallium zinc oxide) transistors and inverters having an atomic layer deposition (ALD) Al2O3 gate insulator on a flexible polyethylene terephthalate (PET) substrate is proposed, that enables a reliability-aware circuit simulation so that the operation of the flexible transistor and circuit can be predicted with high accuracy; in [250] an in-depth study of three-dimensional inkjet-printed flexible organic field-effect transistors (FETs) and integrated circuits (ICs) is reported, highlighting the necessity of modelling-driven design and analysis. With respect to such a consideration, compact modelling of the flexible printed organic FETs has been performed together with SPICE simulations of both static and dynamic behaviors of flexible printed organic circuits. This work provides insights that can fuel further improvement towards the realization of increasingly complex organic ICs and flexible electronic applications.
In addition to efforts to integrate flexible device models into SPICE, other studies have been done to simulate the mechanical deformations of devices and the related variation of electrical properties. In [251] both the biaxial and uniaxial bending stresses in polysilicon TFTs using process conditions like thermal mismatch among materials have been modeled, in [252] a framework based on the nonlinear finite element technique for obtaining stress/strain mapping from the deformation gradient for isotropic materials is developed, that in [253] has been applied to flexible electronics application. In [254] a simulation approach for evaluating the performance of arbitrarily deformed flexible electronic components is presented, that exploits a computer graphic method for three-dimensional object manipulation. The method has been validated by estimating the impact of twisting and crumpling on the performance of flexible RF antennas.
Finally, to underline the importance of the availability of tools of simulation and CAD of advanced flexible devices, we point out that important software companies, and not just individual researchers or laboratories, are investing in this direction [255,256].
With a simulator having all the required features available, the design flow of a flexible electronic circuit is not conceptually different from that of a conventional circuit (Figure 10). The greater complexity arises from the more numerous and varied information that each issue of the CAD software (models, technology files, libraries) must contain in order to take into consideration not only the physical and electrical aspects of the circuit, but also the mechanical ones. Furthermore, the variety of materials used for flexible electronics is certainly much wider than that of materials used for conventional electronics. We also recall that for the design flow in Figure 10 we are considering the already available and optimized substrate. If this were not the case, it would be necessary to insert a further design step aimed at a better functionalization of the substrate.
The current commercial tools [255,256] are still more aimed at a rigid-flex type design rather than a full-flex design, but provide new rules to bridge the MCAD–ECAD (mechanical CAD—electrical CAD) domains. Since reliability is the key, design rules are typically focused on the degradation of the system in the transition zone between rigid and flexible substrates and on the flexible substrate. Rules usually include the minimum bending radius, avoiding placing vias in bend areas or transition zones, avoiding placing component pads too close to the bend area, and, finally, avoiding placing stiffeners that can interfere with the bend radius and are too close to vias and pins. An inter-layer checker by means of a configurable matrix of custom DRCs (design rule checks) ensures meeting the requirements for rigid-flex designs of conductor and non-conductor layers such as soldermask, coverlay, stiffener, and adhesives. This kind of automated approach allows saving up to 50% of the project time for rigid-flex projects. Clearly, as we move towards fully flexible system design, the degree of CAD complexity will increase. First, the database of materials for flexible substrates should be extended (currently only the main plastic materials are planned) and the same for active layers. In rigid-flex design, usually the flex part of the project includes connectors, while in a fully flexible design, the core of the circuit/device is on the substrate region to be bent. Therefore, a more accurate modeling of the various layers and materials from a mechanical point of view is needed.

4.3. New Characterization Methods

In addition to the design method and production processes, the way of characterizing the reliability of electronic devices is also inevitably changing.
First of all, a deep understanding of the charge transport mechanism of new materials (organic, biodegradable, biocompatible) is mandatory. In this regard, the technique of low-frequency noise measurements could be strategic [257]. This technique, long established for the characterization of conventional electronic devices, being highly sensitive and non-destructive, now finds application for the study of sensors, 2D materials [257], organic materials [258,259,260,261,262,263] and could therefore be used for the characterization of new flexible devices, in addition to conventional electrical characterization methods, such as current-voltage, impedance, dielectric. While usually the reliability of electronic devices is verified by electrical characterization, even under conditions of accelerated stress to obtain their lifetime, now it is also essential to carry out a mechanical characterization. In particular, in recent years, the characterization of flexible devices based on the cycles and the radius of bending has become fundamental [264,265,266,267,268] and a few researchers have dedicated themselves to the development of bending test machines [269,270,271,272]. In this regard, the technique of low-frequency noise measurements, previously mentioned as a valid tool for studying the characteristics of materials and devices, has also proven to be a sensitive tool for the characterization of the degradation of electron devices on flexible substrates as a consequence of mechanical stress [272]. Other works have also faced stretching stress [273] and various deformations tests [274] are nowadays performed to characterize the flexible samples. Figure 11, reproduced from [274], shows the schematics and actual photographs of various deformation characterizations that are implemented by the proposed test apparatus: (a) linear bending mode; (b) twisting mode; (c) stretching mode; (d) sliding mode; (e) shearing mode. Although it is only an example, Figure 10 schematizes all the mechanical stresses that the flexible circuits should be subjected to for a complete performance evaluation. Although devices with this potential are not yet widespread, it would be a good solution to perform both electrical and mechanical characterization simultaneously, to better understand the correlation between mechanical stress and electrical properties and to monitor eventual changes in real time. Much work still needs to be done in this field, to perfect the various techniques and make them available in all research laboratories.

5. Conclusions

In this brief review, the main solutions of flexible devices for IoT applications that are fabricated with the goal of environment sustainability are reviewed. RFID, sensors, memories and energy harvesters implemented in biodegradable materials, with eco-friendly structures and fabricated with environmentally sustainable processes are described. The first evident limitation of the analyzed technologies is that, except for some very rare examples in the literature, they are not yet able to be integrated and to supply more complex flexible electronic systems. The production of flexible electronics is therefore still limited, almost exclusively, to the manufacture of single devices or rather small and simple systems. Much research still needs to be done in this direction, also evaluating the possibility of resorting to mature and effective technologies in industry that have driven the development of traditional rigid semiconductor devices and that can also be potentially applicable to flexible devices. The integration of flexible technologies with ultra-thin chips based on silicon, the semiconductor with the best performances in electronics, would combine flexibility and scalability (for sensing, actuating, energy harvesting) of the new flexible devices with the high efficiency of silicon for computation and communication purposes. However, although this way would certainly lead to very high-performance systems, integration with silicon would slow down the achievement of the goals in terms of environmental sustainability, also considering the ever-increasing number of nodes within an IoT network and the number of networks itself. According to the materials, both organic and inorganic materials are promising for sustainable flexible electronics, both for substrates and active layers. However, further research and development are required to improve stability, functionality, and both electrical and mechanical performances. Manufacturing technologies, and above all printing technologies, still need to be optimized, especially as currently process variability remains a challenge. The simplicity of disassembly processes in the end-of-life phase of the devices must also be evaluated for the choice of materials, in order to guarantee the possibility of recycling or biodegradability. Finally, the skills required for designers of electronic circuits are changing, as they must have competences not only on the electrical properties of materials and the physics of conventional devices, but also on materials science and mechanics. Even the simulation and design tools for electronic circuits must include features dedicated to the design of flexible circuits with organic materials and the characterization of the realized devices must be performed also considering mechanical stresses. At present, there is not yet a professional figure who has all the necessary skills, therefore it is necessary that the work in this field is carried out by research groups from different scientific fields. Furthermore, although some CAD software manufacturers are adapting to the new demands, there are still no tools largely available similar to those for conventional circuit design. The same goes for the equipment needed for the characterization of new flexible electronic systems, for which researchers almost always have to resort to self-made instrumentation and equipment.

Author Contributions

Conceptualization, G.S.; methodology, G.S.; validation, C.C. and A.A.; investigation, G.S.; writing—original draft preparation, G.S.; writing—review and editing, C.C. and A.A.; visualization, C.C.; supervision, G.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data sharing not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Weiss, E.B. United Nations Conference on Environment and Development. Int. Leg. Mater. 1992, 31, 814–817. [Google Scholar] [CrossRef]
  2. Whaiduzzaman, M.; Barros, A.; Chanda, M.; Barman, S.; Sultana, T.; Rahman, M.S.; Roy, S.; Fidge, C. A Review of Emerging Technologies for IoT-Based Smart Cities. Sensors 2022, 22, 9271. [Google Scholar] [CrossRef] [PubMed]
  3. Deepaisarn, S.; Yiwsiw, P.; Chaisawat, S.; Lerttomolsakul, T.; Cheewakriengkrai, L.; Tantiwattanapaibul, C.; Buaruk, S.; Sornlertlamvanich, V. Automated Street Light Adjustment System on Campus with AI-Assisted Data Analytics. Sensors 2023, 23, 1853. [Google Scholar] [CrossRef] [PubMed]
  4. García-Castellano, M.; González-Romo, J.M.; Gómez-Galán, J.A.; García-Martín, J.P.; Torralba, A.; Pérez-Mira, V. ITERL: A Wireless Adaptive System for Efficient Road Lighting. Sensors 2019, 19, 5101. [Google Scholar] [CrossRef] [PubMed]
  5. Abarro, C.C.; Caliwag, A.C.; Valverde, E.C.; Lim, W.; Maier, M. Implementation of IoT-Based Low-Delay Smart Streetlight Monitoring System. IEEE Internet Things J. 2022, 9, 18461. [Google Scholar] [CrossRef]
  6. Liu, C.-H.; Hsiao, C.-Y.; Gu, J.-C.; Liu, K.-Y.; Yan, S.-F.; Chiu, C.H.; Ho, M.C. HCL Control Strategy for an Adaptive Roadway Lighting Distribution. Appl. Sci. 2021, 11, 9960. [Google Scholar] [CrossRef]
  7. Ordaz-García, O.O.; Ortiz-López, M.; Quiles-Latorre, F.J.; Arceo-Olague, J.G.; Solís-Robles, R.; Bellido-Outeiriño, F.J. DALI Bridge FPGA-Based Implementation in a Wireless Sensor Node for IoT Street Lighting Applications. Electronics 2020, 9, 1803. [Google Scholar] [CrossRef]
  8. Guerrero-Ulloa, G.; Andrango-Catota, A.; Abad-Alay, M.; Hornos, M.J.; Rodríguez-Domínguez, C. Development and Assessment of an Indoor Air Quality Control IoT-Based System. Electronics 2023, 12, 608. [Google Scholar] [CrossRef]
  9. Kim, J.; Bang, J.; Choi, A.; Moon, H.J.; Sung, M. Estimation of Occupancy Using IoT Sensors and a Carbon Dioxide-Based Machine Learning Model with Ventilation System and Differential Pressure Data. Sensors 2023, 23, 585. [Google Scholar] [CrossRef]
  10. Rollo, F.; Bachechi, C.; Po, L. Anomaly Detection and Repairing for Improving Air Quality Monitoring. Sensors 2023, 23, 640. [Google Scholar] [CrossRef]
  11. Zhu, Y.; Al-Ahmed, S.A.; Shakir, M.Z.; Olszewska, J.I. LSTM-Based IoT-Enabled CO2 Steady-State Forecasting for Indoor Air Quality Monitoring. Electronics 2023, 12, 107. [Google Scholar] [CrossRef]
  12. Hawchar, A.; Ould, S.; Bennett, N.S. Carbon Dioxide Monitoring inside an Australian Brewery Using an Internet-of-Things Sensor Network. Sensors 2022, 22, 9752. [Google Scholar] [CrossRef] [PubMed]
  13. García, L.; Garcia-Sanchez, A.-J.; Asorey-Cacheda, R.; Garcia-Haro, J.; Zúñiga-Cañón, C.-L. Smart Air Quality Monitoring IoT-Based Infrastructure for Industrial Environments. Sensors 2022, 22, 9221. [Google Scholar] [CrossRef]
  14. Starace, G.; Tiwari, A.; Colangelo, G.; Massaro, A. Advanced Data Systems for Energy Consumption Optimization and Air Quality Control in Smart Public Buildings Using a Versatile Open Source Approach. Electronics 2022, 11, 3904. [Google Scholar] [CrossRef]
  15. Kharbouch, A.; Berouine, A.; Elkhoukhi, H.; Berrabah, S.; Bakhouya, M.; El Ouadghiri, D.; Gaber, J. Internet-of-Things Based Hardware-in-the-Loop Framework for Model-Predictive-Control of Smart Building Ventilation. Sensors 2022, 22, 7978. [Google Scholar] [CrossRef]
  16. Yasin, A.; Delaney, J.; Cheng, C.-T.; Pang, T.Y. The Design and Implementation of an IoT Sensor-Based Indoor Air Quality Monitoring System Using Off-the-Shelf Devices. Appl. Sci. 2022, 12, 9450. [Google Scholar] [CrossRef]
  17. Khan, M.A.; Kim, H.-c.; Park, H. Leveraging Machine Learning for Fault-Tolerant Air Pollutants Monitoring for a Smart City Design. Electronics 2022, 11, 3122. [Google Scholar] [CrossRef]
  18. Alvear-Puertas, V.E.; Burbano-Prado, Y.A.; Rosero-Montalvo, P.D.; Tözün, P.; Marcillo, F.; Hernandez, W. Smart and Portable Air-Quality Monitoring IoT Low-Cost Devices in Ibarra City, Ecuador. Sensors 2022, 22, 7015. [Google Scholar] [CrossRef] [PubMed]
  19. Pastor-Fernández, A.; Cerezo-Narváez, A.; Montero-Gutiérrez, P.; Ballesteros-Pérez, P.; Otero-Mateo, M. Use of Low-Cost Devices for the Control and Monitoring of CO2 Concentration in Existing Buildings after the COVID Era. Appl. Sci. 2022, 12, 3927. [Google Scholar] [CrossRef]
  20. Montanaro, T.; Sergi, I.; Basile, M.; Mainetti, L.; Patrono, L. An IoT-Aware Solution to Support Governments in Air Pollution Monitoring Based on the Combination of Real-Time Data and Citizen Feedback. Sensors 2022, 22, 1000. [Google Scholar] [CrossRef]
  21. Sridhar, K.; Radhakrishnan, P.; Swapna, G.; Kesavamoorthy, R.; Pallavi, L.; Thiagarajan, R. A modular IOT sensing platform using hybrid learning ability for air quality prediction. Meas. Sens. 2023, 25, 100609. [Google Scholar] [CrossRef]
  22. Fadda, M.; Anedda, M.; Girau, R.; Pau, G.; Giusto, D.D. A Social Internet of Things Smart City Solution for Traffic and Pollution Monitoring in Cagliari. IEEE Internet Things J. 2023, 10, 2373. [Google Scholar] [CrossRef]
  23. Meng, Q.; Lu, P.; Zhu, S. A Smartphone-enabled IoT System for Vibration and Noise Monitoring of Rail Transit. IEEE Internet Things J. 2023, 10, 8907. [Google Scholar] [CrossRef]
  24. Alashaikh, A.S.; Alhazemi, F.M. Efficient Mobile Crowdsourcing for Environmental Noise Monitoring. IEEE Access 2022, 10, 77251. [Google Scholar] [CrossRef]
  25. Segura-Garcia, J.; Calero, J.M.A.; Pastor-Aparicio, A.; Marco-Alaez, R.; Felici-Castell, S.; Wang, Q. 5G IoT System for Real-Time Psycho-Acoustic Soundscape Monitoring in Smart Cities with Dynamic Computational Offloading to the Edge. IEEE Internet Things J. 2021, 8, 12467. [Google Scholar] [CrossRef]
  26. Monti, L.; Vincenzi, M.; Mirri, S.; Pau, G.; Salomoni, P. RaveGuard: A Noise Monitoring Platform Using Low-End Microphones and Machine Learning. Sensors 2020, 20, 5583. [Google Scholar] [CrossRef]
  27. Zhang, X.; Zhao, M.; Dong, R. Time-Series Prediction of Environmental Noise for Urban IoT Based on Long Short-Term Memory Recurrent Neural Network. Appl. Sci. 2020, 10, 1144. [Google Scholar] [CrossRef]
  28. Mydlarz, C.; Sharma, M.; Lockerman, Y.; Steers, B.; Silva, C.; Bello, J.P. The Life of a New York City Noise Sensor Network. Sensors 2019, 19, 1415. [Google Scholar] [CrossRef]
  29. Segura Garcia, J.; Pérez Solano, J.J.; Cobos Serrano, M.; Navarro Camba, E.A.; Felici Castell, S.; Soriano Asensi, A.; Montes Suay, F. Spatial Statistical Analysis of Urban Noise Data from a WASN Gathered by an IoT System: Application to a Small City. Appl. Sci. 2016, 6, 380. [Google Scholar] [CrossRef]
  30. Arisdakessian, S.; Wahab, O.A.; Mourad, A.; Otrok, H.; Guizani, M. A Survey on IoT Intrusion Detection: Federated Learning, Game Theory, Social Psychology, and Explainable AI as Future Directions. IEEE Internet Things J. 2023, 10, 4059. [Google Scholar] [CrossRef]
  31. Lorenzo, O.G.; Suárez-García, A.; Peña, D.G.; Fuente, M.G.; Granados-López, D. A Low-Cost Luxometer Benchmark for Solar Illuminance Measurement System Based on the Internet of Things. Sensors 2022, 22, 7107. [Google Scholar] [CrossRef] [PubMed]
  32. Al-Begain, K.; Khan, M.; Alothman, B.; Joumaa, C.; Alrashed, E. A DDoS Detection and Prevention System for IoT Devices and Its Application to Smart Home Environment. Appl. Sci. 2022, 12, 11853. [Google Scholar] [CrossRef]
  33. Jhuang, Y.-Y.; Yan, Y.-H.; Horng, G.-J. GDPR Personal Privacy Security Mechanism for Smart Home System. Electronics 2023, 12, 831. [Google Scholar] [CrossRef]
  34. Perumal, T.; Ramanujam, E.; Suman, S.; Sharma, A.; Singhal, H. Internet of Things Centric-Based Multiactivity Recognition in Smart Home Environment. IEEE Internet Things J. 2023, 10, 1724. [Google Scholar] [CrossRef]
  35. Condon, F.; Martínez, J.M.; Eltamaly, A.M.; Kim, Y.-C.; Ahmed, M.A. Design and Implementation of a Cloud-IoT-Based Home Energy Management System. Sensors 2023, 23, 176. [Google Scholar] [CrossRef] [PubMed]
  36. Iliev, Y.; Ilieva, G. A Framework for Smart Home System with Voice Control Using NLP Methods. Electronics 2023, 12, 116. [Google Scholar] [CrossRef]
  37. Xu, B.; Hussain, B.; Wang, Y.; Cheng, H.C.; Yue, C.P. Smart Home Control System Using VLC and Bluetooth Enabled AC Light Bulb for 3D Indoor Localization with Centimeter-Level Precision. Sensors 2022, 22, 8181. [Google Scholar] [CrossRef]
  38. Chen, X.; Fu, Z.; Song, Z.; Yang, L.; Ndifon, A.M.; Su, Z.; Liu, Z.; Gao, S. An IoT and Wearables-Based Smart Home for ALS Patients. IEEE Internet Things J. 2022, 9, 20945. [Google Scholar] [CrossRef]
  39. Barber, R.; Ortiz, F.J.; Garrido, S.; Calatrava-Nicolás, F.M.; Mora, A.; Prados, A.; Vera-Repullo, J.A.; Roca-González, J.; Méndez, I.; Mozos, Ó.M. A Multirobot System in an Assisted Home Environment to Support the Elderly in Their Daily Lives. Sensors 2022, 22, 7983. [Google Scholar] [CrossRef]
  40. Philip, A.; Islam, S.N.; Phillips, N.; Anwar, A. Optimum Energy Management for Air Conditioners in IoT-Enabled Smart Home. Sensors 2022, 22, 7102. [Google Scholar] [CrossRef]
  41. Nyangaresi, V.O.; Abduljabbar, Z.A.; Mutlaq, K.A.-A.; Ma, J.; Honi, D.G.; Aldarwish, A.J.Y.; Abduljaleel, I.Q. Energy Efficient Dynamic Symmetric Key Based Protocol for Secure Traffic Exchanges in Smart Homes. Appl. Sci. 2022, 12, 12688. [Google Scholar] [CrossRef]
  42. Putrada, A.G.; Abdurohman, M.; Perdana, D.; Nuha, H.H. Machine Learning Methods in Smart Lighting Toward Achieving User Comfort: A Survey. IEEE Access 2022, 10, 45137. [Google Scholar] [CrossRef]
  43. Lee, C.-T.; Chen, L.-B.; Chu, H.-M.; Hsieh, C.-J. Design and Implementation of a Leader-Follower Smart Office Lighting Control System Based on IoT Technology. IEEE Access 2022, 10, 28066. [Google Scholar] [CrossRef]
  44. Griva, A.I.; Boursianis, A.D.; Wan, S.; Sarigiannidis, P.; Psannis, K.E.; Karagiannidis, G.; Goudos, S.K. LoRa-Based IoT Network Assessment in Rural and Urban Scenarios. Sensors 2023, 23, 1695. [Google Scholar] [CrossRef] [PubMed]
  45. Rai, S.C.; Nayak, S.P.; Acharya, B.; Gerogiannis, V.C.; Kanavos, A.; Panagiotakopoulos, T. ITSS: An Intelligent Traffic Signaling System Based on an IoT Infrastructure. Electronics 2023, 12, 1177. [Google Scholar] [CrossRef]
  46. Dzemydienė, D.; Burinskienė, A.; Čižiūnienė, K.; Miliauskas, A. Development of E-Service Provision System Architecture Based on IoT and WSNs for Monitoring and Management of Freight Intermodal Transportation. Sensors 2023, 23, 2831. [Google Scholar] [CrossRef]
  47. Xu, H.; Berres, A.; Yoginath, S.B.; Sorensen, H.; Nugent, P.; Severino, J.; Tennille, S.A.; Moore, A.; Jones, W.; Sanyal, J. Smart Mobility in the Cloud: Enabling Real-Time Situational Awareness and Cyber-Physical Control Through a Digital Twin for Traffic. IEEE Trans. Intell. Transp. Syst. 2023, 24, 3145. [Google Scholar] [CrossRef]
  48. Kumar, P.; Kumar, S.V.; Priya, L. Smart and Safety Traffic System for the Vehicles on the Road. In IOT with Smart Systems. Smart Innovation, Systems and Technologies; Choudrie, J., Mahalle, P., Perumal, T., Joshi, A., Eds.; Springer: Singapore, 2023; 312p. [Google Scholar] [CrossRef]
  49. Chakravarty, P.D.; Pandya, J.D.; Dave, A.; Rathod, Y.; Iyer, S.S. Emergency Vehicle-Based Vehicle Detection Model. In Futuristic Trends for Sustainable Development and Sustainable Ecosystems; Ortiz-Rodriguez, F., Ed.; IGI Global: Hershey, PA, USA, 2022; pp. 137–146. [Google Scholar] [CrossRef]
  50. Cao, J.; Zhang, J.; Liu, M.; Yin, S.; An, Y. Green Logistics of Vehicle Dispatch under Smart IoT. Sens. Mater. 2022, 34, 3317. [Google Scholar] [CrossRef]
  51. Mejjaouli, S. Internet of Things based Decision Support System for Green Logistics. Sustainability 2022, 14, 14756. [Google Scholar] [CrossRef]
  52. Raji, C.G.; Shamna, S.K.; Murshidha; Fathimathul, F.V.P.; Shiljiya, K.T. Emergency Vehicles Detection during Traffic Congestion. In Proceedings of the 2022 6th International Conference on Trends in Electronics and Informatics (ICOEI), Tirunelveli, India, 28–30 April 2022; pp. 32–37. [Google Scholar] [CrossRef]
  53. Phan, A.-C.; Trieu, T.-N.; Phan, T.-C. Driver drowsiness detection and smart alerting using deep learning and IoT. Internet Things 2023, 22, 100705. [Google Scholar] [CrossRef]
  54. Kuo, Y.-H.; Leung, J.M.Y.; Yan, Y. Public transport for smart cities: Recent innovations and future challenges. Eur. J. Oper. Res. 2023, 306, 1001. [Google Scholar] [CrossRef]
  55. Rosayyan, P.; Paul, J.; Subramaniam, S.; Ganesan, S.I. An optimal control strategy for emergency vehicle priority system in smart cities using edge computing and IOT sensors. Meas. Sens. 2023, 26, 100697. [Google Scholar] [CrossRef]
  56. Mohammed, K.; Abdelhafid, M.; Kamal, K.; Ismail, N.; Ilias, A. Intelligent driver monitoring system: An Internet of Things-based system for tracking and identifying the driving behavior. Comput. Stand. Interfaces 2023, 84, 103704. [Google Scholar] [CrossRef]
  57. Hari Prasad, S.A.; Kumar, R. IoT cloud system for traffic monitoring and vehicular accidents prevention. AIP Conf. Proc. 2023, 2427, 020055. [Google Scholar] [CrossRef]
  58. Saxena, A.K.; Tripathi, R.C.; Khan, G. Design of a smart public transport system based on IoT. AIP Conf. Proc. 2023, 2427, 020031. [Google Scholar] [CrossRef]
  59. Alanazi, F. Development of Smart Mobility Infrastructure in Saudi Arabia: A Benchmarking Approach. Sustainability 2023, 15, 3158. [Google Scholar] [CrossRef]
  60. ElKashlan, M.; Elsayed, M.S.; Jurcut, A.D.; Azer, M. A Machine Learning-Based Intrusion Detection System for IoT Electric Vehicle Charging Stations (EVCSs). Electronics 2023, 12, 1044. [Google Scholar] [CrossRef]
  61. Liu, D.; Zhang, Y.; Wang, W.; Dev, K.; Khowaja, S.A. Flexible Data Integrity Checking with Original Data Recovery in IoT-Enabled Maritime Transportation Systems. IEEE Trans. Intell. Transp. Syst. 2023, 24, 2618. [Google Scholar] [CrossRef]
  62. Rocha, D.; Teixeira, G.; Vieira, E.; Almeida, J.; Ferreira, J. A Modular In-Vehicle C-ITS Architecture for Sensor Data Collection, Vehicular Communications and Cloud Connectivity. Sensors 2023, 23, 1724. [Google Scholar] [CrossRef]
  63. Ghani Khan, M.U.; Elhadef, M.; Mehmood, A. Intelligent Urban Cities: Optimal Path Selection Based on Ad Hoc Network. IEEE Access 2023, 11, 19259. [Google Scholar] [CrossRef]
  64. Vitali, G.; Arru, M.; Magnanini, E. A Scalable Device for Undisturbed Measurement of Water and CO2 Fluxes through Natural Surfaces. Sensors 2023, 23, 2647. [Google Scholar] [CrossRef]
  65. Zou, X.; Liu, W.; Huo, Z.; Wang, S.; Chen, Z.; Xin, C.; Bai, Y.; Liang, Z.; Gong, Y.; Qian, Y.; et al. Current Status and Prospects of Research on Sensor Fault Diagnosis of Agricultural Internet of Things. Sensors 2023, 23, 2528. [Google Scholar] [CrossRef] [PubMed]
  66. Saban, M.; Bekkour, M.; Amdaouch, I.; El Gueri, J.; Ait Ahmed, B.; Chaari, M.Z.; Ruiz-Alzola, J.; Rosado-Muñoz, A.; Aghzout, O. A Smart Agricultural System Based on PLC and a Cloud Computing Web Application Using LoRa and LoRaWan. Sensors 2023, 23, 2725. [Google Scholar] [CrossRef]
  67. Senoo, E.E.K.; Akansah, E.; Mendonça, I.; Aritsugi, M. Monitoring and Control Framework for IoT, Implemented for Smart Agriculture. Sensors 2023, 23, 2714. [Google Scholar] [CrossRef] [PubMed]
  68. Garg, G.; Gupta, S.; Mishra, P.; Vidyarthi, A.; Singh, A.; Ali, A. CROPCARE: An Intelligent Real-Time Sustainable IoT System for Crop Disease Detection Using Mobile Vision. IEEE Internet Things J. 2023, 10, 2840. [Google Scholar] [CrossRef]
  69. Elashmawy, R.; Uysal, I. Precision Agriculture Using Soil Sensor Driven Machine Learning for Smart Strawberry Production. Sensors 2023, 23, 2247. [Google Scholar] [CrossRef]
  70. Fathy, C.; Ali, H.M. A Secure IoT-Based Irrigation System for Precision Agriculture Using the Expeditious Cipher. Sensors 2023, 23, 2091. [Google Scholar] [CrossRef]
  71. Dutta, M.; Gupta, D.; Sahu, S.; Limkar, S.; Singh, P.; Mishra, A.; Kumar, M.; Mutlu, R. Evaluation of Growth Responses of Lettuce and Energy Efficiency of the Substrate and Smart Hydroponics Cropping System. Sensors 2023, 23, 1875. [Google Scholar] [CrossRef] [PubMed]
  72. Bertocco, M.; Parrino, S.; Peruzzi, G.; Pozzebon, A. Estimating Volumetric Water Content in Soil for IoUT Contexts by Exploiting RSSI-Based Augmented Sensors via Machine Learning. Sensors 2023, 23, 2033. [Google Scholar] [CrossRef]
  73. Contreras-Castillo, J.; Guerrero-Ibañez, J.A.; Santana-Mancilla, P.C.; Anido-Rifón, L. SAgric-IoT: An IoT-Based Platform and Deep Learning for Greenhouse Monitoring. Appl. Sci. 2023, 13, 1961. [Google Scholar] [CrossRef]
  74. Postolache, S.; Sebastião, P.; Viegas, V.; Postolache, O.; Cercas, F. IoT-Based Systems for Soil Nutrients Assessment in Horticulture. Sensors 2023, 23, 403. [Google Scholar] [CrossRef]
  75. Habib, S.; Alyahya, S.; Islam, M.; Alnajim, A.M.; Alabdulatif, A.; Alabdulatif, A. Design and Implementation: An IoT-Framework-Based Automated Wastewater Irrigation System. Electronics 2023, 12, 28. [Google Scholar] [CrossRef]
  76. Azfar, S.; Nadeem, A.; Ahsan, K.; Mehmood, A.; Siddiqui, M.S.; Saeed, M.; Ashraf, M. An IoT-Based System for Efficient Detection of Cotton Pest. Appl. Sci. 2023, 13, 2921. [Google Scholar] [CrossRef]
  77. Singh, R.; Singh, R.; Gehlot, A.; Akram, S.V.; Priyadarshi, N.; Twala, B. Horticulture 4.0: Adoption of Industry 4.0 Technologies in Horticulture for Meeting Sustainable Farming. Appl. Sci. 2022, 12, 12557. [Google Scholar] [CrossRef]
  78. Bristow, N.; Rengaraj, S.; Chadwick, D.R.; Kettle, J.; Jones, D.L. Development of a LoRaWAN IoT Node with Ion-Selective Electrode Soil Nitrate Sensors for Precision Agriculture. Sensors 2022, 22, 9100. [Google Scholar] [CrossRef]
  79. Shaikh, F.K.; Karim, S.; Zeadally, S.; Nebhen, J. Recent Trends in Internet-of-Things-Enabled Sensor Technologies for Smart Agriculture. IEEE Internet Things J. 2022, 9, 23583. [Google Scholar] [CrossRef]
  80. Gamal, Y.; Soltan, A.; Said, L.A.; Madian, H.A.; Radwan, A.G. Smart Irrigation Systems: Overview. IEEE Access, 2023; Early Access. [Google Scholar] [CrossRef]
  81. Nadeem, A.; Chatzichristodoulou, D.; Quddious, A.; Shoaib, N.; Vassiliou, L.; Vryonides, P.; Nikolaou, S. UHF IoT Humidity and Temperature Sensor for Smart Agriculture Applications Powered from an Energy Harvesting System. In Proceedings of the 2022 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS), Bali, Indonesia, 24–26 November 2022; pp. 186–190. [Google Scholar] [CrossRef]
  82. Kour, K.; Gupta, D.; Gupta, K.; Anand, D.; Elkamchouchi, D.H.; Pérez-Oleaga, C.M.; Ibrahim, M.; Goyal, N. Monitoring Ambient Parameters in the IoT Precision Agriculture Scenario: An Approach to Sensor Selection and Hydroponic Saffron Cultivation. Sensors 2022, 22, 8905. [Google Scholar] [CrossRef]
  83. Arrubla-Hoyos, W.; Ojeda-Beltrán, A.; Solano-Barliza, A.; Rambauth-Ibarra, G.; Barrios-Ulloa, A.; Cama-Pinto, D.; Arrabal-Campos, F.M.; Martínez-Lao, J.A.; Cama-Pinto, A.; Manzano-Agugliaro, F. Precision Agriculture and Sensor Systems Applications in Colombia through 5G Networks. Sensors 2022, 22, 7295. [Google Scholar] [CrossRef]
  84. Ryalat, M.; ElMoaqet, H.; AlFaouri, M. Design of a Smart Factory Based on Cyber-Physical Systems and Internet of Things towards Industry 4.0. Appl. Sci. 2023, 13, 2156. [Google Scholar] [CrossRef]
  85. Haricha, K.; Khiat, A.; Issaoui, Y.; Bahnasse, A.; Ouajji, H. Recent technological progress to empower Smart Manufacturing: Review and Potential Guidelines. IEEE Access 2023. [Google Scholar] [CrossRef]
  86. Chen, H.; Jeremiah, S.R.; Lee, C.; Park, J.H. A Digital Twin-Based Heuristic Multi-Cooperation Scheduling Framework for Smart Manufacturing in IIoT Environment. Appl. Sci. 2023, 13, 1440. [Google Scholar] [CrossRef]
  87. Noor-A-Rahim, M.; John, J.; Firyaguna, F.; Sherazi, H.H.R.; Kushch, S.; Vijayan, A.; O’Connell, E.; Pesch, D.; O’Flynn, B.; O’Brien, W.; et al. Wireless Communications for Smart Manufacturing and Industrial IoT: Existing Technologies, 5G and Beyond. Sensors 2023, 23, 73. [Google Scholar] [CrossRef] [PubMed]
  88. Hsu, C.-H.; Cheng, S.-J.; Chang, T.-J.; Huang, Y.-M.; Fung, C.-P.; Chen, S.-F. Low-Cost and High-Efficiency Electromechanical Integration for Smart Factories of IoT with CNN and FOPID Controller Design under the Impact of COVID-19. Appl. Sci. 2022, 12, 3231. [Google Scholar] [CrossRef]
  89. Yu, W.; Liu, Y.; Dillon, T.; Rahayu, W.; Mostafa, F. An Integrated Framework for Health State Monitoring in a Smart Factory Employing IoT and Big Data Techniques. IEEE Internet Things J. 2022, 9, 2443. [Google Scholar] [CrossRef]
  90. Kwak, K.-J.; Park, J.-M. A Study on Semantic-Based Autonomous Computing Technology for Highly Reliable Smart Factory in Industry 4.0. Appl. Sci. 2021, 11, 10121. [Google Scholar] [CrossRef]
  91. Hsu, T.-C.; Tsai, Y.-H.; Chang, D.-M. The Vision-Based Data Reader in IoT System for Smart Factory. Appl. Sci. 2022, 12, 6586. [Google Scholar] [CrossRef]
  92. Abril-Jiménez, P.; Merino-Barbancho, B.; Fico, G.; Martín Guirado, J.C.; Vera-Muñoz, C.; Mallo, I.; Lombroni, I.; Cabrera Umpierrez, M.F.; Arredondo Waldmeyer, M.T. Evaluating IoT-Based Services to Support Patient Empowerment in Digital Home Hospitalization Services. Sensors 2023, 23, 1744. [Google Scholar] [CrossRef]
  93. Ahmed, S.T.; Kumar, V.; Kim, J. AITel: eHealth Augmented Intelligence based Telemedicine Resource Recommendation Framework for IoT devices in Smart cities. IEEE Internet Things J. 2023. [Google Scholar] [CrossRef]
  94. Le, N.T.; Thwe Chit, M.M.; Truong, T.L.; Siritantikorn, A.; Kongruttanachok, N.; Asdornwised, W.; Chaitusaney, S.; Benjapolakul, W. Deployment of Smart Specimen Transport System Using RFID and NB-IoT Technologies for Hospital Laboratory. Sensors 2023, 23, 546. [Google Scholar] [CrossRef]
  95. Chang, J.; Ong, H.; Wang, T.; Chen, H.-H. A Fully Automated Intelligent Medicine Dispensary System Based on AIoT. IEEE Internet Things J. 2022, 9, 23954. [Google Scholar] [CrossRef]
  96. Rathee, G.; Saini, H.; Kerrache, C.A.; Herrera-Tapia, J. A Computational Framework for Cyber Threats in Medical IoT Systems. Electronics 2022, 11, 1705. [Google Scholar] [CrossRef]
  97. Rybak, G.; Strzecha, K.; Krakós, M. A New Digital Platform for Collecting Measurement Data from the Novel Imaging Sensors in Urology. Sensors 2022, 22, 1539. [Google Scholar] [CrossRef] [PubMed]
  98. Fan, L. Usage of Narrowband Internet of Things in Smart Medicine and Construction of Robotic Rehabilitation System. IEEE Access 2022, 10, 6246. [Google Scholar] [CrossRef]
  99. Nasser, A.R.; Hasan, A.M.; Humaidi, A.J.; Alkhayyat, A.; Alzubaidi, L.; Fadhel, M.A.; Santamaría, J.; Duan, Y. IoT and Cloud Computing in Health-Care: A New Wearable Device and Cloud-Based Deep Learning Algorithm for Monitoring of Diabetes. Electronics 2021, 10, 2719. [Google Scholar] [CrossRef]
  100. Wang, B.; Hu, X.; Zhang, J.; Xu, C.; Gao, Z. Intelligent Internet of Things in Mammography Screening Using Multicenter Transformation between Unified Capsules. IEEE Internet Things J. 2023, 10, 1536. [Google Scholar] [CrossRef]
  101. Firouzi, F.; Jiang, S.; Chakrabarty, K.; Farahani, B.; Daneshmand, M.; Song, J.; Mankodiya, K. Fusion of IoT, AI, Edge–Fog–Cloud, and Blockchain: Challenges, Solutions, and a Case Study in Healthcare and Medicine. IEEE Internet Things J. 2023, 10, 3686. [Google Scholar] [CrossRef]
  102. Kim, B.; Kim, S.; Lee, M.; Chang, H.; Park, E.; Han, T. Application of an Internet of Medical Things (IoMT) to Communications in a Hospital Environment. Appl. Sci. 2022, 12, 12042. [Google Scholar] [CrossRef]
  103. Alsharif, M.H.; Jahid, A.; Kelechi, A.H.; Kannadasan, R. Green IoT: A Review and Future Research Directions. Symmetry 2023, 15, 757. [Google Scholar] [CrossRef]
  104. Khan, F.A.; Noor, R.M.; Kiah, M.L.M.; Ahmedy, I.; Yamani, M.; Soon, T.K.; Ahmad, M. Performance Evaluation and Validation of QCM (Query Control Mechanism) for QoS-Enabled Layered-Based Clustering for Reactive Flooding in the Internet of Things. Sensors 2020, 20, 283. [Google Scholar] [CrossRef]
  105. Hakola, L.; Jansson, E. Sustainable substrate for printed electronics. In Printing for Fabrication 2019: Materials, Applications, and Process—Technical Program and Proceedings; The Society for Imaging Science and Technology, IS&T: Cambridge, MA, USA, 2019; pp. 132–137. [Google Scholar]
  106. Jansson, E.; Lyytikäinen, J.; Tanninen, P.; Eiroma, K.; Leminen, V.; Immonen, K.; Hakola, L. Suitability of Paper-Based Substrates for Printed Electronics. Materials 2022, 15, 957. [Google Scholar] [CrossRef]
  107. Prenzel, T.M.; Gehring, F.; Fuhs, F.; Albrecht, S. Influence of design properties of printed electronics on their environmental profile. Matér. Tech. 2021, 109, 506. [Google Scholar] [CrossRef]
  108. Gudrun, S.; Halvor, K.; Thordur, M. Greenhouse Gas Emissions from Silicon Production -Development of Carbon Footprint with Changing Energy Systems. In Proceedings of the Proceedings of the 16th International Ferro-Alloys Congress (INFACON XVI), Virtual, 12 September 2021. [Google Scholar] [CrossRef]
  109. Khan, Y.; Thielens, A.; Muin, S.; Ting, J.; Baumbauer, C.; Arias, A.C. A New Frontier of Printed Electronics: Flexible Hybrid Electronics. Adv. Mater. 2020, 32, 1905279. [Google Scholar] [CrossRef]
  110. Hussein, R.N.; Schlingman, K.; Noade, C.; Carmichael, R.S.; Carmichael, T.B. Shellac-paper composite as a green substrate for printed electronics. Flex. Print. Electron. 2022, 7, 045007. [Google Scholar] [CrossRef]
  111. Agate, S.; Joyce, M.; Lucia, L.; Pal, L. Cellulose and nanocellulose-based flexible-hybrid printed electronics and conductive composites—A review. Carbohydr. Polym. 2018, 198, 249. [Google Scholar] [CrossRef]
  112. Liyanage, S.; Acharya, S.; Parajuli, P.; Shamshina, J.L.; Abidi, N. Production and Surface Modification of Cellulose Bioproducts. Polymers 2021, 13, 3433. [Google Scholar] [CrossRef] [PubMed]
  113. Koga, H.; Nogi, M. Flexible Paper Electronics. In Organic Electronics Materials and Devices; Ogawa, S., Ed.; Springer: Tokyo, Japan, 2015. [Google Scholar] [CrossRef]
  114. Jaiswal, A.K.; Kumar, V.; Jansson, E.; Huttunen, O.-H.; Yamamoto, A.; Vikman, M.; Khakalo, A.; Hiltunen, J.; Behfar, M.H. Biodegradable Cellulose Nanocomposite Substrate for Recyclable Flexible Printed Electronics. Adv. Electron. Mater. 2023, 9, 2201094. [Google Scholar] [CrossRef]
  115. Liang, Y.; Wei, Z.; Wang, H.E.; Wang, R.; Zhang, X. Flexible freestanding conductive nanopaper based on PPy:PSS nanocellulose composite for supercapacitors with high performance. Sci. China Mater. 2023, 66, 964. [Google Scholar] [CrossRef]
  116. Zhong, J.; Li, G.; Guo, R.; Ning, H.; Zhang, H.; Fang, Z.; Fu, X.; Wei, X.; Yao, R.; Peng, J. Bilayer Metal Oxide Channel Thin Film Transistor with Flat Interface Based on Smooth Transparent Nanopaper Substrate. IEEE Electron Device Lett. 2022, 43, 2113. [Google Scholar] [CrossRef]
  117. Zhang, J.; Liu, D.; Shi, Q.; Yang, B.; Guo, P.; Fang, L.; Dai, S.; Xiong, L. Bioinspired organic optoelectronic synaptic transistors based on cellulose nanopaper and natural chlorophyll-a for neuromorphic systems. Npj Flex Electron. 2022, 6, 30. [Google Scholar] [CrossRef]
  118. Liang, Y.; Wei, Z.; Wang, H.-E.; Flores, M.; Wang, R.; Zhang, X. Flexible and freestanding PANI: PSS/CNF nanopaper electrodes with enhanced electrochemical performance for supercapacitors. J. Power Sources 2022, 548, 232071. [Google Scholar] [CrossRef]
  119. Cunha, I.; Ferreira, S.H.; Martins, J.; Fortunato, E.; Gaspar, D.; Martins, R.; Pereira, L. Foldable and Recyclable Iontronic Cellulose Nanopaper for Low-Power Paper. Electron. Adv. Sustain. Syst. 2022, 6, 2200177. [Google Scholar] [CrossRef]
  120. Li, Z.; Zhou, J.; Zhong, J. Nanocellulose Paper for Flexible Electronic Substrate. In Emerging Nanotechnologies in Nanocellulose; Hu, L., Jiang, F., Chen, C., Eds.; NanoScience and Technology: Danville, CA, USA, 2023; 211p. [Google Scholar] [CrossRef]
  121. Moon, R.J.; Schueneman, G.T.; Simonsen, J. Overview of Cellulose Nanomaterials, Their Capabilities and Applications. JOM 2016, 68, 2383. [Google Scholar] [CrossRef]
  122. Varshney, S.; Mishra, N.; Gupta, M.K. Progress in nanocellulose and its polymer based composites: A review on processing, characterization, and applications. Polym. Compos. 2021, 42, 3660. [Google Scholar] [CrossRef]
  123. Liu, W.; Liu, K.; Du, H.; Zheng, T.; Zhang, N.; Xu, T.; Pang, B.; Zhang, X.; Si, C. Cellulose Nanopaper: Fabrication, Functionalization, and Applications. Nano-Micro. Lett. 2022, 14, 104. [Google Scholar] [CrossRef] [PubMed]
  124. Lizundia, E.; Delgado-Aguilar, M.; Mutjé, P.; Fernández, E.; Robles-Hernandez, B.; de la Fuente, M.R.; Vilas, J.L. Cu-coated cellulose nanopaper for green and low-cost electronics. Cellulose 2016, 23, 1997. [Google Scholar] [CrossRef]
  125. Shi, C.; Wu, Z.; Xu, J.; Wu, Q.; Li, D.; Chen, G.; He, M.; Tian, J. Fabrication of transparent and superhydrophobic nanopaper via coating hybrid SiO2/MWCNTs composite. Carbohydr. Polym. 2019, 225, 115229. [Google Scholar] [CrossRef]
  126. Seydibeyoğlu, M.Ö.; Dogru, A.; Wang, J.; Rencheck, M.; Han, Y.; Wang, L.; Seydibeyoğlu, E.A.; Zhao, X.; Ong, K.; Shatkin, J.A.; et al. Review on Hybrid Reinforced Polymer Matrix Composites with Nanocellulose, Nanomaterials, and Other Fibers. Polymers 2023, 15, 984. [Google Scholar] [CrossRef]
  127. Faraco, T.A.; Fontes, M.d.L.; Paschoalin, R.T.; Claro, A.M.; Gonçalves, I.S.; Cavicchioli, M.; Farias, R.L.d.; Cremona, M.; Ribeiro, S.J.L.; Barud, H.d.S.; et al. Review of Bacterial Nanocellulose as Suitable Substrate for Conformable and Flexible Organic Light-Emitting Diodes. Polymers 2023, 15, 479. [Google Scholar] [CrossRef]
  128. Jain, K.; Wang, Z.; Garma, L.D.; Engel, E.; Ciftci, G.C.; Fager, C.; Larsson, P.A.; Wågberg, L. 3D printable composites of modified cellulose fibers and conductive polymers and their use in wearable electronics. Appl. Mater. Today 2023, 30, 101703. [Google Scholar] [CrossRef]
  129. Chen, Z.; Hu, Y.; Shi, G.; Zhuo, H.; Ali, M.A.; Jamróz, E.; Zhang, H.; Zhong, L.; Peng, X. Advanced Flexible Materials from Nanocellulose. Adv. Funct. Mater. 2023, 33, 2214245. [Google Scholar] [CrossRef]
  130. Wang, X.; Li, X.; Wang, B.; Chen, J.; Zhang, L.; Zhang, K.; He, M.; Xue, Y.; Yang, G. Preparation of Salt-Induced Ultra-Stretchable Nanocellulose Composite Hydrogel for Self-Powered Sensors. Nanomaterials 2023, 13, 157. [Google Scholar] [CrossRef] [PubMed]
  131. Duroc, Y. From Identification to Sensing: RFID Is One of the Key Technologies in the IoT Field. Sensors 2022, 22, 7523. [Google Scholar] [CrossRef] [PubMed]
  132. Bukova, B.; Tengler, J.; Brumercikova, E.; Brumercik, F.; Kissova, O. Environmental Burden Case Study of RFID Technology in Logistics Centre. Sensors 2023, 23, 1268. [Google Scholar] [CrossRef]
  133. Wilczkiewicz, B.; Jankowski-Mihułowicz, P.; Węglarski, M. Test Platform for Developing Processes of Autonomous Identification in RFID Systems with Proximity-Range Read/Write Devices. Electronics 2023, 12, 617. [Google Scholar] [CrossRef]
  134. Gendy, M.E.G.; Tham, P.; Harrison, F.; Yuce, M.R. Comparing Efficiency and Performance of IoT BLE and RFID-Based Systems for Achieving Contract Tracing to Monitor Infection Spread among Hospital and Office Staff. Sensors 2023, 23, 1397. [Google Scholar] [CrossRef]
  135. Altaf, S.; Haroon, M.; Ahmad, S.; Nasr, E.A.; Zaindin, M.; Huda, S.; Rehman, Z.u. Radio-Frequency-Identification-Based 3D Human Pose Estimation Using Knowledge-Level Technique. Electronics 2023, 12, 374. [Google Scholar] [CrossRef]
  136. Chen, K.; Ma, Y.; Liu, H.; Liang, X.; Fu, Y. Trajectory-Robust RFID Relative Localization Based on Phase Profile Correlation. IEEE Trans. Instrum. Meas. 2023, 72, 8000613. [Google Scholar] [CrossRef]
  137. Mahapatra, S.; Kannan, V.; Seshadri, S.; Ravi, V.; Sofana Reka, S. An IoT-Based Wristband for Automatic People Tracking, Contact Tracing and Geofencing for COVID-19. Sensors 2022, 22, 9902. [Google Scholar] [CrossRef]
  138. Osmólska, E.; Stoma, M.; Starek-Wójcicka, A. Application of Biosensors, Sensors, and Tags in Intelligent Packaging Used for Food Products—A Review. Sensors 2022, 22, 9956. [Google Scholar] [CrossRef]
  139. Zhao, Y.; Zhao, X.; Li, L.; Liu, X.; Li, Q. Timing: Tag Interference Modeling for RFID Localization in Dense Deployment. IEEE Sens. J. 2022, 22, 23464. [Google Scholar] [CrossRef]
  140. Li, D.; Cao, W.; Wang, C.; Tong, Y. UHF RFID reader antenna with switchable far-field and near-field working state. Electron. Lett. 2022, 58, 931. [Google Scholar] [CrossRef]
  141. Benedetti, D.; Maselli, G. Robust RFID Tag Identification. Sensors 2022, 22, 8406. [Google Scholar] [CrossRef] [PubMed]
  142. Lubna; Zahid, A.; Mufti, N.; Ullah, S.; Nawaz, M.W.; Sharif, A.; Imran, M.A.; Abbasi, Q.H. IoT Enabled Vacant Parking Slot Detection System Using Inkjet-printed RFID Tags. IEEE Sens. J. 2023, 23, 7828. [Google Scholar] [CrossRef]
  143. Sharif, A.; Althobaiti, T.; Alotaibi, A.A.; Ramzan, N.; Imran, M.A.; Abbasi, Q.H. Inkjet-Printed UHF RFID Sticker for Traceability and Spoilage Sensing of Fruits. IEEE Sens. J. 2023, 23, 733. [Google Scholar] [CrossRef]
  144. Raso, E.; Bianco, G.M.; Bracciale, L.; Marrocco, G.; Occhiuzzi, C.; Loreti, P. Privacy-Aware Architectures for NFC and RFID Sensors in Healthcare Applications. Sensors 2022, 22, 9692. [Google Scholar] [CrossRef]
  145. Zohra, F.T.; Salim, O.; Masoumi, H.; Karmakar, N.C.; Dey, S. Health Monitoring of Conveyor Belt Using UHF RFID and Multi-Class Neural Networks. Electronics 2022, 11, 3737. [Google Scholar] [CrossRef]
  146. Song, Z.; Rahmadya, B.; Sun, R.; Takeda, S. An RFID-Based Wireless Vibration and Physical-Shock Sensing System Using Edge Processing. IEEE Sens. J. 2022, 22, 20010. [Google Scholar] [CrossRef]
  147. Solar, H.; Beriain, A.; Rezola, A.; del Rio, D.; Berenguer, R. A 22-m Operation Range Semi-Passive UHF RFID Sensor Tag with Flexible Thermoelectric Energy Harvester. IEEE Sens. J. 2022, 22, 19797. [Google Scholar] [CrossRef]
  148. Montanaro, T.; Sergi, I.; Motroni, A.; Buffi, A.; Nepa, P.; Pirozzi, M.; Catarinucci, L.; Colella, R.; Chietera, F.P.; Patrono, L. An IoT-Aware Smart System Exploiting the Electromagnetic Behavior of UHF-RFID Tags to Improve Worker Safety in Outdoor Environments. Electronics 2022, 11, 717. [Google Scholar] [CrossRef]
  149. Behera, S.K. Chipless RFID Sensors for Wearable Applications: A Review. IEEE Sens. J. 2022, 22, 1105. [Google Scholar] [CrossRef]
  150. Subrahmannian, A.; Behera, S.K. Chipless RFID Sensors for IoT-Based Healthcare Applications: A Review of State of the Art. IEEE Trans. Instrum. Meas. 2022, 71, 1. [Google Scholar] [CrossRef]
  151. Das, R.; Chang, Y.H.; Dyson, M. RFID Forecast, Players and Opportunities 2022–2032, The Complete Analysis of the Global RFID Industry. Available online: https://www.idtechex.com/en/research-report/rfid-forecasts-players-and-opportunities-2022-2032/849 (accessed on 1 May 2023).
  152. Condemi, A.; Cucchiella, F.; Schettini, D. Circular Economy and E-Waste: An Opportunity from RFID TAGs. Appl. Sci. 2019, 9, 3422. [Google Scholar] [CrossRef]
  153. Wang, Y.; Yan, C.; Cheng, S.-Y.; Xu, Z.-Q.; Sun, X.; Xu, Y.-H.; Chen, J.-J.; Jiang, Z.; Liang, K.; Feng, Z.-S. Flexible RFID Tag Metal Antenna on Paper-Based Substrate by Inkjet Printing Technology. Adv. Funct. Mater. 2019, 29, 1902579. [Google Scholar] [CrossRef]
  154. Huang, X.J.; Wang, S.C.; Xie, F.; Tong, M.S. Design of an UHF RFID Tag Antenna with a Paper Substrate. In Proceedings of the 2018 IEEE International Symposium on Antennas and Propagation & USNC/URSI National Radio Science Meeting, Boston, MA, USA, 8–13 July 2018; pp. 1363–1364. [Google Scholar]
  155. Morales-Guerra, J.; Umaña-Idarraga, F.; Giraldo-Escobar, W.; Gonzalez-Valencia, E.; Reyes-Vera, E. Performance analysis of a Compact, Flexible and Biodegradable UHF RFID Tag Antenna. In Proceedings of the 2021 International Conference on Electromagnetics in Advanced Applications (ICEAA), Honolulu, HI, USA, 9–13 August 2021; pp. 357–360. [Google Scholar] [CrossRef]
  156. Gupta, D.; Sood, D.; Yu, M.; Kumar, M. Compact Biodegradable UHF RFID Tag for Short Life Cycle Applications. In Proceedings of the 2021 IEEE Indian Conference on Antennas and Propagation (InCAP), Jaipur, India, 13–16 December 2021; pp. 399–401. [Google Scholar] [CrossRef]
  157. Kim, S. Inkjet-Printed Electronics on Paper for RF Identification (RFID) and Sensing. Electronics 2020, 9, 1636. [Google Scholar] [CrossRef]
  158. Kumar, M.; Sharma, A.; Zuazola, I.J.G. A biodegradable multi-platform tolerant passive UHF RFID tag antenna for short-life cycle IoT applications. In Proceedings of the 2021 IEEE Indian Conference on Antennas and Propagation (InCAP), Jaipur, India, 13–16 December 2021; pp. 391–394. [Google Scholar] [CrossRef]
  159. Wang, Y.; Huang, Y.; Li, Y.-Z.; Cheng, P.; Cheng, S.-Y.; Liang, Q.; Xu, Z.-Q.; Chen, H.-J.; Feng, Z.-S. A facile process combined with roll-to-roll flexographic printing and electroless deposition to fabricate RFID tag antenna on paper substrates. Compos. Part B Eng. 2021, 224, 109194. [Google Scholar] [CrossRef]
  160. Sidibe, A.; Mir, L.L.; Dhuiège, B.; Depres, G.; Takacs, A.; Mennekens, J. A Thin Paper UHF Antenna on Nanocelloluse Based Substrate for Battery-free Geolocation Tags. In Proceedings of the 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (AP-S/URSI), Denver, CO, USA, 10–15 July 2022; pp. 125–126. [Google Scholar] [CrossRef]
  161. Hussain, M.; Amin, Y.; Lee, K.-G. A Compact and Flexible UHF RFID Tag Antenna for Massive IoT Devices in 5G System. Sensors 2020, 20, 5713. [Google Scholar] [CrossRef] [PubMed]
  162. Mostaccio, G.; Bianco, M.; Amendola, S.; Marrocco, G.; Occhiuzzi, C. RFID for Food Industry 4.0—Current Trends and Monitoring of Fruit Ripening. In Proceedings of the 2022 IEEE 12th International Conference on RFID Technology and Applications (RFID-TA), Cagliari, Italy, 12–14 September 2022; pp. 109–112. [Google Scholar] [CrossRef]
  163. Machiels, J.; Appeltans, R.; Bauer, D.K.; Segers, E.; Henckens, Z.; Van Rompaey, W.; Adons, D.; Peeters, R.; Geiβler, M.; Kuehnoel, K.; et al. Screen Printed Antennas on Fiber-Based Substrates for Sustainable HF RFID Assisted E-Fulfilment Smart Packaging. Materials 2021, 14, 5500. [Google Scholar] [CrossRef] [PubMed]
  164. Marques, A.H.F.; dos Santos, D.; de Oliveira Vieira, K.; Quadros, M.H.; Rebello, P.H.P.; Ferro, V.L.D.; Santos, E.D.; Fellegara, H.; Valério, P.; Fugikawa-Santos, L.; et al. Environmentally Friendly, Semi-transparent, Screen Printed Antenna for RFID Tag Applications. Braz. J. Phys. 2021, 51, 434. [Google Scholar] [CrossRef]
  165. StoraEnso. Available online: https://www.storaenso.com/en/newsroom/news/2020/1/eco-rfid-explained--a-look-behind-the-worlds-greenest-tag (accessed on 1 May 2023).
  166. BioplasticsNew. Available online: https://bioplasticsnews.com/2020/01/12/stora-enso-sustainable-rfid-tag/ (accessed on 1 May 2023).
  167. Avery Dennison. Available online: https://rfid.averydennison.com/en/home/products-solutions/rfid-sustainable-tags.html (accessed on 1 May 2023).
  168. Yang, W.; Cheng, X.; Guo, Z.; Sun, Q.; Wanga, J.; Wang, C. Design, fabrication and applications of flexible RFID antennas based on printed electronic materials and technologies. J. Mater. Chem. C 2023, 11, 406. [Google Scholar] [CrossRef]
  169. Piro, B.; Tran, H.V.; Thu, V.T. Sensors Made of Natural Renewable Materials: Efficiency, Recyclability or Biodegradability—The Green Electronics. Sensors 2020, 20, 5898. [Google Scholar] [CrossRef]
  170. Siti, F.K.; Mariatti, M.; Jang-Kyo, K. Green Strategies to Printed Sensors for Healthcare Applications. Polym. Rev. 2020, 61, 116. [Google Scholar] [CrossRef]
  171. Kalambate, P.K.; Rao, Z.; Wu, D.J.; Shen, Y.; Boddula, R.; Huang, Y. Electrochemical (bio) sensors go green. Biosens. Bioelectron. 2020, 163, 112270. [Google Scholar] [CrossRef] [PubMed]
  172. Liu, Y.; Shang, S.; Mo, S.; Wang, P.; Wang, H. Eco-friendly Strategies for the Material and Fabrication of Wearable Sensors. Int. J. Precis. Eng. Manuf.-Green Tech. 2021, 8, 1323. [Google Scholar] [CrossRef]
  173. Ponnamma, D.; Parangusan, H.; Deshmukh, K.; Kar, P.; Muzaffar, A.; Pasha, S.K.K.; Ahamed, M.B.; Al-Maadeed, M.A.A. Green synthesized materials for sensor, actuator, energy storage and energy generation: A review. Polym.-Plast. Technol. Mater. 2020, 59, 1–62. [Google Scholar] [CrossRef]
  174. Guan, M.; Liu, Y.; Du, H.; Long, Y.; An, X.; Liu, H.; Cheng, B. Durable, breathable, sweat-resistant, and degradable flexible sensors for human motion detection. Chem. Eng. J. 2023, 462, 142151. [Google Scholar] [CrossRef]
  175. Altay, B.N.; Aksoy, B.; Banerjee, D.; Maddipatla, D.; Fleming, P.D.; Bolduc, M.; Cloutier, S.G.; Atashbar, M.Z.; Gupta, R.B.; Demir, M. Lignin-Derived Carbon-Coated Functional Paper for Printed Electronics. ACS Appl. Electron. Mater. 2021, 3, 3904. [Google Scholar] [CrossRef]
  176. Zhao, P.; Zhang, R.; Tong, Y.; Zhao, X.; Tang, Q.; Liu, Y. All-Paper, All-Organic, Cuttable, and Foldable Pressure Sensor with Tuneable Conductivity Polypyrrole. Adv. Electron. Mater. 2020, 6, 1901426. [Google Scholar] [CrossRef]
  177. Sun, B.; Chen, Y.; Zhou, G.; Zhou, Y.; Guo, T.; Zhu, S.; Mao, S.; Zhao, Y.; Shao, J.; Li, Y. A Flexible Corn Starch-Based Biomaterial Device Integrated with Capacitive-Coupled Memristive Memory, Mechanical Stress Sensing, Synapse, and Logic Operation Functions. Adv. Electron. Mater. 2023, 9, 2201017. [Google Scholar] [CrossRef]
  178. Ma, X.; Hu, Q.; Dai, Y.; He, P.; Zhang, X. Disposable sensors based on biodegradable polylactic acid piezoelectret films and their application in wearable electronics. Sens. Actuators A Phys. 2022, 346, 113834. [Google Scholar] [CrossRef]
  179. Ketabi, M.; Al Shboul, A.; Mahinnezhad, S.; Izquierdo, R. Aerosol-jet printing of flexible green graphene humidity sensors for IoT applications. In Proceedings of the 2021 IEEE Sensors, Sydney, Australia, 31 October–3 November 2021; pp. 1–4. [Google Scholar] [CrossRef]
  180. Liu, X.; Fu, T.; Ward, J.; Gao, H.; Yin, B.; Woodard, T.; Lovley, D.R.; Yao, J. Multifunctional Protein Nanowire Humidity Sensors for Green Wearable Electronics. Adv. Electron. Mater. 2020, 6, 2000721. [Google Scholar] [CrossRef]
  181. Ma, J.; Jiang, Y.; Shen, L.; Ma, H.; Sun, T.; Lv, F.; Kiran, A.; Zhu, N. Wearable biomolecule smartsensors based on one-step fabricated berlin green printed arrays. Biosens. Bioelectron. 2019, 144, 111637. [Google Scholar] [CrossRef] [PubMed]
  182. Xiang, H.; Li, Z.; Liu, H.; Chen, T.; Zhou, H.; Huang, W. Green flexible electronics based on starch. Npj Flex Electron 2022, 6, 15. [Google Scholar] [CrossRef]
  183. Xu, M.; Cai, H.; Liu, Z.; Chen, F.; Chen, L.; Chen, X.; Cheng, X.; Dai, F.; Li, Z. Breathable, Degradable Piezoresistive Skin Sensor Based on a Sandwich Structure for High-Performance Pressure Detection. Adv. Electron. Mater. 2021, 7, 2100368. [Google Scholar] [CrossRef]
  184. Tang, D.; Abdalkarim, S.Y.H.; Dong, Y.; Yu, H.-Y. One-pot strategy to fabricate conductive cellulose nanocrystal-polyethylenedioxythiophene nanocomposite: Synthesis mechanism, modulated morphologies and sensor assembly. Carbohydr. Polym. 2023, 311, 120758. [Google Scholar] [CrossRef]
  185. Li, C.; Li, G.; Li, G.; Yu, D.; Song, Z.; Liu, X.; Wang, H.; Liu, W. Cellulose Fiber-Derived Carbon Fiber Networks for Durable Piezoresistive Pressure Sensing. ACS Appl. Electron. Mater. 2021, 3, 2389. [Google Scholar] [CrossRef]
  186. Ko, W.-Y.; Huang, L.-T.; Lin, K.-J. Green technique solvent-free fabrication of silver nanoparticle–carbon nanotube flexible films for wearable sensors. Sens. Actuators A Phys. 2021, 317, 112437. [Google Scholar] [CrossRef]
  187. Lahcen, A.A.; Rauf, S.; Beduk, T.; Durmus, C.; Aljedaibi, A.; Timur, S.; Alshareef, H.N.; Amine, A.; Wolfbeis, O.S.; Salama, K.N. Electrochemical sensors and biosensors using laser-derived graphene: A comprehensive review. Biosens. Bioelectron. 2020, 168, 112565. [Google Scholar] [CrossRef]
  188. Ismail, Z. Laser writing of graphene on cellulose paper and analogous material for green and sustainable electronic: A concise review. Carbon Lett. 2022, 32, 1227. [Google Scholar] [CrossRef]
  189. Singh, A.T.; Lantigua, D.; Meka, A.; Taing, S.; Pandher, M.; Camci-Unal, G. Paper-Based Sensors: Emerging Themes and Applications. Sensors 2018, 18, 2838. [Google Scholar] [CrossRef]
  190. Tai, H.; Duan, Z.; Wang, Y.; Wang, S.; Jiang, Y. Paper-Based Sensors for Gas, Humidity, and Strain Detections: A Review. ACS Appl. Mater. Interfaces 2020, 12, 31037. [Google Scholar] [CrossRef]
  191. Korotcenkov, G. Paper-Based Humidity Sensors as Promising Flexible Devices: State of the Art: Part 1. General Consideration. Nanomaterials 2023, 13, 1110. [Google Scholar] [CrossRef] [PubMed]
  192. Korotcenkov, G.; Simonenko, N.P.; Simonenko, E.P.; Sysoev, V.V.; Brinzari, V. Paper-Based Humidity Sensors as Promising Flexible Devices, State of the Art, Part 2: Humidity-Sensor Performances. Nanomaterials 2023, 13, 1381. [Google Scholar] [CrossRef] [PubMed]
  193. Duan, Z.; Yuan, Z.; Jiang, Y.; Yuan, L.; Tai, H. Amorphous carbon material of daily carbon ink: Emerging applications in pressure, strain, and humidity sensors. J. Mater. Chem. C 2023, 11, 5585. [Google Scholar] [CrossRef]
  194. Liu, H.; Xiang, H.; Li, Z.; Meng, Q.; Li, P.; Ma, Y.; Zhou, H.; Huang, W. Flexible and degradable multimodal sensor fabricated by transferring laser-induced porous carbon on starch film. ACS Sustain. Chem. Eng. 2020, 8, 527. [Google Scholar] [CrossRef]
  195. Liu, S.; Chen, C.; Zhang, D.; Dong, G.; Zheng, D.; Jiang, Y.; Zhou, G.; Liu, J.-M.; Kempa, K.; Gao, J. Recyclable and flexible starch-Ag networks and its application in joint sensor. Nanoscale Res. Lett. 2019, 14, 127. [Google Scholar] [CrossRef]
  196. Zhang, S.; Li, H.; Yang, Z.; Chen, B.; Li, K.; Lai, X.; Zeng, X. Degradable and stretchable bio-based strain sensor for human motion detection. J. Colloid Interface Sci. 2022, 626, 554. [Google Scholar] [CrossRef]
  197. Liu, H.; Xiang, H.; Ma, Y.; Li, Z.; Meng, Q.; Li, P.; Zhou, H.; Huang, W. Flexible, Degradable, and Cost-Effective Strain Sensor Fabricated by a Scalable Papermaking Procedure. ACS Sustain. Chem. Eng. 2018, 6, 15749. [Google Scholar] [CrossRef]
  198. Liu, X.; Wang, X.; Liu, Y.; Yao, Y.; Zhu, X.; Hu, Y.; Wan, T.; Cheng, B. Synthesis of Poly(ether carbonate)-Based Polyurethane for Biodegradable–Recyclable Pressure Sensors. ACS Sustain. Chem. Eng. 2023, 11, 4258. [Google Scholar] [CrossRef]
  199. Rivadeneyra, A.; Marín-Sánchez, A.; Wicklein, B.; Salmerón, J.F.; Castillo, E.; Bobinger, M.; Salinas-Castillo, A. Cellulose nanofibers as substrate for flexible and biodegradable moisture sensors. Compos. Sci. Technol. 2021, 208, 108738. [Google Scholar] [CrossRef]
  200. Yoshida, A.; Wang, Y.-F.; Tachibana, S.; Hasegawa, A.; Sekine, T.; Takeda, Y.; Hong, J.; Kumaki, D.; Shiba, T.; Tokito, S. Printed, all-carbon-based flexible humidity sensor using a cellulose nanofiber/graphene nanoplatelet composite. Carbon Trends 2022, 7, 100166. [Google Scholar] [CrossRef]
  201. Falco, A.; Marín-Sánchez, A.; Loghin, F.C.; Castillo, E.; Salinas-Castillo, A.; Salmerón, J.F.; Rivadeneyra, A. Paper and Salt: Biodegradable NaCl-Based Humidity Sensors for Sustainable Electronics. Front. Electron. 2022, 3, 838472. [Google Scholar] [CrossRef]
  202. Kumar, R.; Rahman, H.; Ranwa, S.; Kumar, A.; Kumar, G. Development of cost effective metal oxide semiconductor based gas sensor over flexible chitosan/PVP blended polymeric substrate. Carbohydr. Polym. 2020, 239, 116213. [Google Scholar] [CrossRef]
  203. Molina, A.; Oliva, J.; Oliva, A.I.; Garces, L.; Rodriguez-Gonzalez, V. Enhancing the gas detection response of biodegradable NO2 sensors by creating on their surface oxygen-vacancies/zinc-interstitial defects. Synth. Met. 2023, 295, 117348. [Google Scholar] [CrossRef]
  204. Shahrbabaki, Z.; Farajikhah, S.; Ghasemian, M.B.; Oveissi, F.; Rath, R.J.; Yun, J.; Dehghani, F.; Naficy, S. A Flexible and Polymer-Based Chemiresistive CO2 Gas Sensor at Room Temperature. Adv. Mater. Technol. 2023, 8, 2201510. [Google Scholar] [CrossRef]
  205. Zhang, W.; Zhang, X.; Wu, Z.; Abdurahman, K.; Cao, Y.; Duan, H.; Jia, D. Mechanical, electromagnetic shielding and gas sensing properties of flexible cotton fiber/polyaniline composites. Compos. Sci. Technol. 2020, 188, 107966. [Google Scholar] [CrossRef]
  206. Arena, A.; Branca, C.; Ciofi, C.; D’Angelo, G.; Romano, V.; Scandurra, G. Polypyrrole and Graphene Nanoplatelets Inks as Electrodes for Flexible Solid-State Supercapacitor. Nanomaterials 2021, 11, 2589. [Google Scholar] [CrossRef]
  207. Rajan, K.; Garofalo, E.; Chiolerio, A. Wearable Intrinsically Soft, Stretchable, Flexible Devices for Memories and Computing. Sensors 2018, 18, 367. [Google Scholar] [CrossRef] [PubMed]
  208. Yan, K.; Li, J.; Pan, L.; Shi, Y. Inkjet printing for flexible and wearable electronics. APL Mater. 2020, 8, 120705. [Google Scholar] [CrossRef]
  209. Jia, H.; Gu, S.-Y.; Chang, K. 3D printed self-expandable vascular stents from biodegradable shape memory polymer. Adv. Polym. Technol. 2018, 37, 3222–3228. [Google Scholar] [CrossRef]
  210. Gao, H.; Li, J.; Zhang, F.; Liu, Y.; Leng, J. The research status and challenges of shape memory polymer-based flexible electronics. Mater. Horiz. 2019, 6, 931–944. [Google Scholar] [CrossRef]
  211. Delfag, M.; Rachovitis, G.; Gonzalez, Y.; Jehn, J.; Youssef, A.H.; Schindler, C.; Ruediger, A. Fully printed ZnO-based valency-change memories for flexible and transparent applications. Flex. Print. Electron. 2022, 7, 045001. [Google Scholar] [CrossRef]
  212. Tang, P.; Chen, J.; Qiu, T.; Ning, H.; Fu, X.; Li, M.; Xu, Z.; Luo, D.; Yao, R.; Peng, J. Recent Advances in Flexible Resistive Random Access Memory. Appl. Syst. Innov. 2022, 5, 91. [Google Scholar] [CrossRef]
  213. Huang, W.-Y.; Chang, Y.-C.; Sie, Y.-F.; Yu, C.-R.; Wu, C.-Y.; Hsu, Y.-L. Bio-Cellulose Substrate for Fabricating Fully Biodegradable Resistive Random Access Devices. ACS Appl. Polym. Mater. 2021, 3, 4478. [Google Scholar] [CrossRef]
  214. Arshad, N.; Irshad, M.S.; Abbasi, M.S.; Rehman, S.U.; Ahmed, I.; Javed, M.Q.; Ahmad, S.; Sharaf, M.; Al Firdausi, M.D. Green thin film for stable electrical switching in a low-cost washable memory device: Proof of concept. RSC Adv. 2021, 11, 4327. [Google Scholar] [CrossRef] [PubMed]
  215. Jiang, T.; Meng, X.; Zhou, Z.; Wu, Y.; Tian, Z.; Liu, Z.; Lu, G.; Eginlidil, M.; Yu, H.-D.; Liu, J.; et al. Highly flexible and degradable memory electronics comprised of all-biocompatible materials. Nanoscale 2021, 13, 724. [Google Scholar] [CrossRef] [PubMed]
  216. Raeis-Hosseini, N.; Lee, J.-S. Controlling the resistive switching behavior in starch-based flexible biomemristors. ACS Appl. Mater. Interfaces 2016, 8, 7326. [Google Scholar] [CrossRef]
  217. Lam, J.-Y.; Jang, G.-W.; Huang, C.-J.; Tung, S.-H.; Chen, W.-C. Environmentally Friendly Resistive Switching Memory Devices with DNA as the Active Layer and Bio-Based Polyethylene Furanoate as the Substrate. ACS Sustain. Chem. Eng. 2020, 8, 5100. [Google Scholar] [CrossRef]
  218. Chang, Y.-C.; Lee, C.-J.; Wang, L.-W.; Wang, Y.-H. Air-stable gelatin composite memory devices on a paper substrate. Org. Electron. 2019, 65, 77. [Google Scholar] [CrossRef]
  219. Xinglong, J.; Li, S.; Shuai, Z.; Yu, J.; Kian, L.; Chao, W.; Rong, Z. Biodegradable and Flexible Resistive Memory for Transient Electronics. J. Phys. Chem. C 2018, 122, 16909–16915. [Google Scholar] [CrossRef]
  220. Wang, L.; Zhang, Y.; Zhang, P.; Wen, D. Flexible Transient Resistive Memory Based on Biodegradable Composites. Nanomaterials 2022, 12, 3531. [Google Scholar] [CrossRef]
  221. Rahmani, H.; Shetty, D.; Wagih, M.; Ghasempour, Y.; Palazzi, V.; Carvalho, N.B.; Correia, R.; Costanzo, A.; Vital, D.; Alimenti, F.; et al. Next-Generation IoT Devices: Sustainable Eco-Friendly Manufacturing, Energy Harvesting, and Wireless Connectivity. IEEE J. Microw. 2023, 3, 237. [Google Scholar] [CrossRef]
  222. Thakur, A.; Devi, P. Paper-based flexible devices for energy harvesting, conversion and storage applications: A review. Nano Energy 2022, 94, 106927. [Google Scholar] [CrossRef]
  223. He, J.; Qian, S.; Niu, X.; Zhang, N.; Qian, J.; Hou, X.; Mu, J.; Geng, W.; Chou, X. Piezoelectric-enhanced triboelectric nanogenerator fabric for biomechanical energy harvesting. Nano Energy 2019, 64, 103933. [Google Scholar] [CrossRef]
  224. López, O.L.; Alves, H.; Souza, R.D.; Montejo-Sánchez, S.; Fernández, E.M.G.; Latva-Aho, M. Massive wireless energy transfer: Enabling sustainable IoT toward 6G era. IEEE Internet Things J. 2021, 8, 8816. [Google Scholar] [CrossRef]
  225. Yang, X.; Daoud, W.A. An experimental and computational investigation of (α-methylbenzylidene)carbene. Adv. Funct. Mater. 2016, 26, 8194. [Google Scholar] [CrossRef]
  226. Briscoe, J.; Dunn, S. Piezoelectric nanogenerators—A review of nanostructured piezoelectric energy harvesters. Nano Energy 2015, 14, 15. [Google Scholar] [CrossRef]
  227. Rana, M.M.; Khan, A.A.; Zhu, W.; Al Fattah, F.; Kokilathasan, S.; Rassel, S.; Bernard, R.; Ababou-Girard, S.; Turban, P.; Xu, S.; et al. Enhanced piezoelectricity in lead-free halide perovskite nanocomposite for self-powered wireless electronics. Nano Energy 2022, 101, 107631. [Google Scholar] [CrossRef]
  228. Martinez-Lopez, A.G.; Tinoco, J.C.; Elvira-Hernández, E.A.; Herrera-May, A.L. Solution-processed ZnO energy harvester devices based on flexible substrates. Microsyst. Technol. 2023, 29, 205. [Google Scholar] [CrossRef]
  229. Pattipaka, S.; Bae, Y.M.; Jeong, C.K.; Park, K.-I.; Hwang, G.-T. Perovskite Piezoelectric-Based Flexible Energy Harvesters for Self-Powered Implantable and Wearable IoT Devices. Sensors 2022, 22, 9506. [Google Scholar] [CrossRef] [PubMed]
  230. Sahu, M.; Hajra, S.; Panda, S.; Rajaitha, M.; Panigrahi, B.K.; Rubahn, H.-G.; Mishra, Y.K.; Kim, H.J. Waste textiles as the versatile triboelectric energy-harvesting platform for self-powered applications in sports and athletics. Nano Energy 2022, 97, 107208. [Google Scholar] [CrossRef]
  231. Naval, S.; Jain, A.; Mallick, D. Direct current triboelectric nanogenerators: A review. J. Micromech. Microeng. 2023, 33, 013001. [Google Scholar] [CrossRef]
  232. Dong, X.; Liu, Z.; Yang, P.; Chen, X. Harvesting Wind Energy Based on Triboelectric Nanogenerators. Nanoenergy Adv. 2022, 2, 245. [Google Scholar] [CrossRef]
  233. Syamini, J.; Chandran, A. Mylar Interlayer-Mediated Performance Enhancement of a Flexible Triboelectric Nanogenerator for Self-Powered Pressure Sensing Application. ACS Appl. Electron. Mater. 2023, 5, 1002. [Google Scholar] [CrossRef]
  234. Singh, V.; Singh, B. MoS2-PVDF/PDMS based flexible hybrid piezo-triboelectric nanogenerator for harvesting mechanical energy. J. Alloy. Compd. 2023, 941, 168850. [Google Scholar] [CrossRef]
  235. Hajra, S.; Padhan, A.M.; Sahu, M.; Alagarsamy, P.; Lee, K.; Kim, H.J. Lead-free flexible Bismuth Titanate-PDMS composites: A multifunctional colossal dielectric material for hybrid piezo-triboelectric nanogenerator to sustainably power portable electronics. Nano Energy 2021, 89, 106316. [Google Scholar] [CrossRef]
  236. Sahu, M.; Vivekananthan, V.; Hajra, S.; Abisegapriyan, K.S.; Raj, N.P.M.J.; Kim, S.-J. Synergetic enhancement of energy harvesting performance in triboelectric nanogenerator using ferroelectric polarization for self-powered IR signaling and body activity monitoring. J. Mater. Chem. A 2020, 8, 22257. [Google Scholar] [CrossRef]
  237. Varghese, H.; Abdul Hakkeem, H.M.; Chauhan, K.; Thouti, E.; Pillai, S.; Chandran, A. A high-performance flexible triboelectric nanogenerator based on cellulose acetate nanofibers and micropatterned PDMS films as mechanical energy harvester and self-powered vibrational sensor. Nano Energy 2022, 98, 107339. [Google Scholar] [CrossRef]
  238. Sriphan, S.; Vittayakorn, N. Hybrid piezoelectric-triboelectric nanogenerators for flexible electronics: Recent advances and perspectives. J. Sci. Adv. Mater. Devices 2022, 7, 100461. [Google Scholar] [CrossRef]
  239. Van Toan, N.; Tuoi, T.T.K.; Sui, H.; Trung, N.H.; Samat, K.F.; Ono, T. Ultra-flexible thermoelectric generator based on silicone rubber sheet and electrodeposited thermoelectric material for waste heat harvesting. Energy Rep. 2022, 8, 5026. [Google Scholar] [CrossRef]
  240. Kim, S.; Na, Y.; Nam, C.; Jeong, C.K.; Kim, K.T.; Park, K.-I. Highly tailorable, ultra-foldable, and resorbable thermoelectric paper for origami-enabled energy generation. Nano Energy 2022, 103 Pt A, 107824. [Google Scholar] [CrossRef]
  241. Zhang, Y.; Park, S.-J. Flexible Organic Thermoelectric Materials and Devices for Wearable Green Energy Harvesting. Polymers 2019, 11, 909. [Google Scholar] [CrossRef] [PubMed]
  242. Bakytbekov, A.; Nguyen, T.Q.; Zhang, G.; Strano, M.S.; Salama, K.N.; Shamim, A. Synergistic multi-source ambient RF and thermal energy harvester for green IoT applications. Energy Rep. 2023, 9, 1875. [Google Scholar] [CrossRef]
  243. Yu, B.-Y.; Wang, Z.-H.; Ju, L.; Zhang, C.; Liu, Z.-G.; Tao, L.; Lu, W.-B. Flexible and Wearable Hybrid RF and Solar Energy Harvesting System. IEEE Trans. Antennas Propag. 2022, 70, 2223. [Google Scholar] [CrossRef]
  244. SCAPS-1D. Available online: https://scaps.elis.ugent.be/ (accessed on 1 May 2023).
  245. Tara, A.; Bharti, V.; Sharma, S.; Gupta, R. Device Simulation of FASnI3 Based Perovskite Solar Cell with Zn(O0.3,S0.7) as Electron Transport Layer Using SCAPS-1D. Opt. Mater. 2021, 119, 111362. [Google Scholar] [CrossRef]
  246. Husainat, A.; Ali, W.; Cofie, P.; Attia, J.; Fuller, J. Simulation and Analysis of Methylammonium Lead Iodide (CH3NH3PbI3) Perovskite Solar Cell with Au Contact Using SCAPS 1D Simulator. Am. J. Opt. Photonics 2019, 7, 33. [Google Scholar] [CrossRef]
  247. Hasanzadeh Azar, M.; Aynehband, S.; Abdollahi, H.; Alimohammadi, H.; Rajabi, N.; Angizi, S.; Kamraninejad, V.; Teimouri, R.; Mohammadpour, R.; Simchi, A. SCAPS Empowered Machine Learning Modelling of Perovskite Solar Cells: Predictive Design of Active Layer and Hole Transport Materials. Photonics 2023, 10, 271. [Google Scholar] [CrossRef]
  248. Valletta, A.; Demirkol, A.S.; Maira, G.; Frasca, M.; Vinciguerra, V.; Occhipinti, L.G.; Fortuna, L.; Mariucci, L.; Fortunato, G. A Compact SPICE Model for Organic TFTs and Applications to Logic Circuit Design. IEEE Trans. Nanotechnol. 2016, 15, 754. [Google Scholar] [CrossRef]
  249. Kim, J.-H.; Seo, Y.; Jang, J.T.; Park, S.; Kang, D.; Park, J.; Han, M.; Kim, C.; Park, D.-W.; Kim, D.H. Reliability-Aware SPICE Compatible Compact Modeling of IGZO Inverters on a Flexible Substrate. Appl. Sci. 2021, 11, 4838. [Google Scholar] [CrossRef]
  250. Jung, S.; Kwon, J.; Tokito, S.; Horowitz, G.; Bonnassieux, Y.; Jung, S. Compact modelling and SPICE simulation for three-dimensional, inkjet-printed organic transistors, inverters and ring oscillators. J. Phys. D Appl. Phys. 2019, 52, 444005. [Google Scholar] [CrossRef]
  251. Kong, S.; Lim, H.; Hoessinger, Q.; Guichard, E. TCAD modeling of mechanical stress for simulation of thin film transistor on flexible substrate. SID Symp Dig. Tech Pap. 2019, 50, 1606. [Google Scholar] [CrossRef]
  252. Lim, H.; Kong, S.; Guichard, E.; Hoessinger, A. A general approach for deformation induced stress on flexible electronics. In Proceedings of the International Conference on Simulation of Semiconductor Processes and Devices (SISPAD), Austin, TX, USA, 24–26 September 2018. [Google Scholar]
  253. Dash, T.; Mohapatra, E.; Maiti, C.K. Deformation-induced stress/strain mapping and performance evaluation of a-IGZO thin-film transistors for flexible electronic applications. J. Soc. Inf. Disp. 2021, 29, 130. [Google Scholar] [CrossRef]
  254. Vukovic, A.; Altinozen, A.; Dimitrijevic, T.; Sewell, P. Simulation Platform for Flexible Electronics. In Proceedings of the 2021 15th International Conference on Advanced Technologies, Systems and Services in Telecommunications (TELSIKS), Nis, Serbia, 20–22 October 2021; pp. 135–138. [Google Scholar] [CrossRef]
  255. SILVACO. Available online: https://silvaco.com/webinar/spice-modeling-for-flexible-electronics/ (accessed on 1 May 2023).
  256. CADENCE-PCB. Available online: https://resources.pcb.cadence.com/blog/2023-designing-a-flex-pcb-prototype-pcb-design-tips-and-considerations (accessed on 29 May 2023).
  257. Scandurra, G.; Ciofi, C.; Smulko, J.; Wen, H. A review of design approaches for the implementation of low-frequency noise measurement systems. Rev. Sci. Instrum. 2022, 93, 111101. [Google Scholar] [CrossRef]
  258. Song, Y.; Lee, T. Electronic noise analyses on organic electronic devices. J. Mater. Chem. C 2017, 5, 7123. [Google Scholar] [CrossRef]
  259. Landi, G.; Pagano, S.; Neitzert, H.C.; Mauro, C.; Barone, C. Noise Spectroscopy: A Tool to Understand the Physics of Solar Cells. Energies 2023, 16, 1296. [Google Scholar] [CrossRef]
  260. Battistoni, S.; Sajapin, R.; Erokhin, V.; Verna, A.; Cocuzza, M.; Marasso, S.L.; Iannotta, S. Effects of noise sourcing on organic memristive devices. Chaos Solitons Fractals 2020, 141, 110319. [Google Scholar] [CrossRef]
  261. Ke, L.; Zhao, X.Y.; Kumar, R.S.; Chua, S.J. Low-frequency noise measurement and analysis in organic light-emitting diodes. IEEE Electron Device Lett. 2006, 27, 7–555. [Google Scholar] [CrossRef]
  262. Martin, S.; Dodabalapur, A.; Bao, Z.; Crone, B.; Katz, H.E.; Li, W.; Passner, A.; Rogers, J.A. Flicker noise properties of organic thin-film transistors. J. Appl. Phys. 2000, 87, 3381. [Google Scholar] [CrossRef]
  263. Barone, C.; Maccagnani, P.; Dinelli, F.; Bertoldo, M.; Capelli, R.; Cocchi, M.; Seri, M.; Pagano, S. Electrical conduction and noise spectroscopy of sodium-alginate gold-covered ultrathin films for flexible green electronics. Sci. Rep. 2022, 12, 9861. [Google Scholar] [CrossRef]
  264. Fu, Z.; Hannula, M.; Jauho, A.; Väisänen, K.-L.; Välimäki, M.; Keskinen, J.; Mäntysalo, M. Cyclic Bending Reliability and Failure Mechanism of Printed Biodegradable Flexible Supercapacitor on Polymer Substrate. ACS Appl. Mater. Interfaces 2022, 14, 40145. [Google Scholar] [CrossRef]
  265. Jeong, J.-H.; Kim, J.-H.; Oh, C.-S. Quantitative evaluation of bending reliability for a flexible near-field communication tag. Microelectron. Reliab. 2017, 75, 121. [Google Scholar] [CrossRef]
  266. Suhaimi, M.I.; Nordin, A.N.; Md Ralib, A.A.; Voiculescu, I.; Mak, W.C.; Ming, L.L.; Samsudin, Z. Mechanical durability of screen-printed flexible silver traces for wearable devices. Sens. Bio-Sens. Res. 2022, 38, 100537. [Google Scholar] [CrossRef]
  267. Kim, T.; Kim, J.; Yun, H.; Lee, J.-S.; Lee, J.-H.; Song, J.-Y.; Joo, Y.-C.; Lee, W.-J.; Kim, B.-J. Electrical Reliability of Flexible Silicon Package Integrated on Polymer Substrate during Repeated Bending Deformations. ASME J. Electron. Packag. 2022, 144, 041017. [Google Scholar] [CrossRef]
  268. Maita, F.; Maiolo, L.; Minotti, A.; Pecora, A.; Ricci, D.; Metta, G.; Scandurra, G.; Giusi, G.; Ciofi, C.; Fortunato, G. Ultraflexible Tactile Piezoelectric Sensor Based on Low-Temperature Polycrystalline Silicon Thin-Film Transistor Technology. IEEE Sens. J. 2015, 15, 3819. [Google Scholar] [CrossRef]
  269. Saleh, R.; Barth, M.; Eberhardt, W.; Zimmermann, A. Bending Setups for Reliability Investigation of Flexible Electronics. Micromachines 2021, 12, 78. [Google Scholar] [CrossRef] [PubMed]
  270. Kovac, O.; Lukacs, P. Automatic Evaluation of Flexible Electronic Bending Test. In Proceedings of the 2021 44th International Spring Seminar on Electronics Technology (ISSE), Bautzen, Germany, 5–9 May 2021; pp. 1–5. [Google Scholar] [CrossRef]
  271. Wright, D.N.; Vardøy, A.S.; Belle, B.D.; Taklo, M.M.V. Bending machine for testing reliability of flexible electronics. In Proceedings of the 2017 IMAPS Nordic Conference on Microelectronics Packaging (NordPac), Gothenburg, Sweden, 18–20 June 2017; pp. 47–52. [Google Scholar] [CrossRef]
  272. Scandurra, G.; Arena, A.; Giusi, G.; Cannatà, G.; Ciofi, C. Low frequency noise measurements as an early indicator of degradation for devices on plastic substrates subjected to mechanical stress. In Proceedings of the 2017 International Conference on Noise and Fluctuations (ICNF), Vilnius, Lithuania, 20–23 June 2017; pp. 1–4. [Google Scholar] [CrossRef]
  273. Harris, K.D.; Elias, A.L.; Chung, H.J. Flexible electronics under strain: A review of mechanical characterization and durability enhancement strategies. J. Mater. Sci. 2016, 51, 2771. [Google Scholar] [CrossRef]
  274. Kim, C.; Kim, C.H. Universal Testing Apparatus Implementing Various Repetitive Mechanical Deformations to Evaluate the Reliability of Flexible Electronic Devices. Micromachines 2018, 9, 492. [Google Scholar] [CrossRef]
Figure 1. Flexible electronics is an important building block for the creation of a sustainable and interconnected world.
Figure 1. Flexible electronics is an important building block for the creation of a sustainable and interconnected world.
Sensors 23 05264 g001
Figure 2. Collocation of this paper with respect to other review papers on “green IoT”. In this work we focused on solutions for IoT that are contextually flexible and eco-friendly, and we want to highlight how the skills of electronic circuit designers and the features of simulation and CAD (computer aided design) software are changing to accomplish modern IoT systems designs.
Figure 2. Collocation of this paper with respect to other review papers on “green IoT”. In this work we focused on solutions for IoT that are contextually flexible and eco-friendly, and we want to highlight how the skills of electronic circuit designers and the features of simulation and CAD (computer aided design) software are changing to accomplish modern IoT systems designs.
Sensors 23 05264 g002
Figure 3. Typical architecture of an IoT system. In this figure the four principal layers are shown, but the more complex IoT architectures may have other further layers.
Figure 3. Typical architecture of an IoT system. In this figure the four principal layers are shown, but the more complex IoT architectures may have other further layers.
Sensors 23 05264 g003
Figure 4. Representation of the layers that make up an eco-friendly RFID. With respect to conventional RFID, a green RFID has no plastic substrate and presents fewer adhesive materials. The production process does not involve the use of toxic or environmentally harmful chemicals.
Figure 4. Representation of the layers that make up an eco-friendly RFID. With respect to conventional RFID, a green RFID has no plastic substrate and presents fewer adhesive materials. The production process does not involve the use of toxic or environmentally harmful chemicals.
Sensors 23 05264 g004
Figure 5. Synthetic schematization of the four fronts on which to operate to produce environmentally sustainable sensors: (1) the use of eco-friendly materials as substrates; (2) as sensing layers; (3) as coating or encapsulation; (4) the implementation of sustainable fabrication processes. For each front identified, the most current solutions based on sustainable materials and production processes are illustrated.
Figure 5. Synthetic schematization of the four fronts on which to operate to produce environmentally sustainable sensors: (1) the use of eco-friendly materials as substrates; (2) as sensing layers; (3) as coating or encapsulation; (4) the implementation of sustainable fabrication processes. For each front identified, the most current solutions based on sustainable materials and production processes are illustrated.
Sensors 23 05264 g005
Figure 6. Structure of a RRAM. (a) RRAM top view; (b) flexed RRAM. Both substrate and storage layer are implemented with eco-friendly materials.
Figure 6. Structure of a RRAM. (a) RRAM top view; (b) flexed RRAM. Both substrate and storage layer are implemented with eco-friendly materials.
Sensors 23 05264 g006
Figure 7. Summary of the different types of environmental energy with the relative devices used to harvest.
Figure 7. Summary of the different types of environmental energy with the relative devices used to harvest.
Sensors 23 05264 g007
Figure 8. Schematization of the mandatory skills for a designer of flexible, green electronic circuits.
Figure 8. Schematization of the mandatory skills for a designer of flexible, green electronic circuits.
Sensors 23 05264 g008
Figure 9. Representation of the new features that are required for modern simulation and CAD software.
Figure 9. Representation of the new features that are required for modern simulation and CAD software.
Sensors 23 05264 g009
Figure 10. Design flux of a flexible circuit. It is not conceptually different from that of a conventional circuit, but each step is more complex.
Figure 10. Design flux of a flexible circuit. It is not conceptually different from that of a conventional circuit, but each step is more complex.
Sensors 23 05264 g010
Figure 11. Deformation characterization of flexible devices. The image is reported from [274] under a CC BY 4.0 license. It shows the schematics and actual photographs of various deformation characterizations that are implemented by the test apparatus proposed in [274]: (a) linear bending mode; (b) twisting mode; (c) stretching mode; (d) sliding mode; (e) shearing mode.
Figure 11. Deformation characterization of flexible devices. The image is reported from [274] under a CC BY 4.0 license. It shows the schematics and actual photographs of various deformation characterizations that are implemented by the test apparatus proposed in [274]: (a) linear bending mode; (b) twisting mode; (c) stretching mode; (d) sliding mode; (e) shearing mode.
Sensors 23 05264 g011
Table 1. Comparison between paper and the most used plastic substrates, in terms of the impact on climate change and resource use.
Table 1. Comparison between paper and the most used plastic substrates, in terms of the impact on climate change and resource use.
Substrate MaterialClimate Change Impact
kg CO2 eq. */Sheet ***
Resource Use
kg Sb eq. **/Sheet ***
Paper1.3 × 10−45.2 × 10−11
PET (polyethylene terephthalate)1.5 × 10−31.8 × 10−10
PEI (polyetherimide)1.3 × 10−22.0 × 10−9
PEEK (polyether ether ketone)7.4 × 10−32.2 × 10−9
* Indicator of potential global warming due to emissions of greenhouse gases to the air. ** Indicator of the depletion of natural non-fossil resources. *** Sheet with 25 cm2 surface area, 125 mm thickness.
Table 2. Comparison between conductivity of the printed layer on paper and PET substrates. The layer thickness used in the volume resistivity measurement was considered to be equal on every substrate. An ink transfer volume of 7 mL/m2 has been considered.
Table 2. Comparison between conductivity of the printed layer on paper and PET substrates. The layer thickness used in the volume resistivity measurement was considered to be equal on every substrate. An ink transfer volume of 7 mL/m2 has been considered.
Printing TechniqueSubstrate MaterialSheet Resistance
mΩ/Square
Volume
Resistivity (Ω·cm)
Flexo-printingP1 *177 ± 192.2 × 10−6
P2 **169 ± 161.6 × 10−6
PET ***260 ± 232.1 × 10−6
Rotary
screen-printing
P145.3 ± 1.34.1 × 10−5
P239.4 ± 0.63.4 × 10−5
PET52.3 ± 2.54.7 × 10−5
* Coated paper, Stora Enso NovaPress Silk, 80 g/m2. ** Coated paper, ultra-smooth top side for printed electronics, Arjo Wiggins PowerCoat HD, 95 g/m2. *** Melinex ST506 (DuPont Teijin Films, Chester, VA, USA).
Table 3. Comparison of Young’s modulus of paper and plastic substrates [109].
Table 3. Comparison of Young’s modulus of paper and plastic substrates [109].
Substrate MaterialYoung’s Modulus [GPa]
PaperUp to 3.5 *
PET 2.8
PEN3.0
PDMSUp to 3.7 **
* Depending on coating. ** Depending on different crosslinking density.
Table 4. Example of biodegradability test on cellulose based and plastic samples. The test duration was 127 days [114].
Table 4. Example of biodegradability test on cellulose based and plastic samples. The test duration was 127 days [114].
Sample *StatusBiodegradation **
CNF 50%, HEC 50%Printed74%
CNF 50%, HEC 50%Unprinted78%
MCCUnprinted94%
TPUUnprintedNo degradation
* CNF: cellulose nanofibrils; HEC: hydroxyethyl cellulose; MCC: microcrystalline cellulose; TPU: thermoplastic polyurethane. ** The data are extrapolated from [114]. Biodegradation of samples was estimated firstly by employing the CO2 evolution method and, secondly, by visually evaluating samples disintegration in soil upon burial.
Table 5. Characteristics and performances of eco-friendly RFIDs. All the reported examples operate in the UHF (ultra-high frequency) band.
Table 5. Characteristics and performances of eco-friendly RFIDs. All the reported examples operate in the UHF (ultra-high frequency) band.
MaterialDimensions
(mm2)
Gain (dBi)Reading Range (m)Ref.
Paper substrate63.6 × 252.37-[155]
Copper ink on paper dielectric substrate81.95 × 14.51.81-[156]
Graphene ink on paper substrate16 × 65−53.5[157]
Bioresorbable copper-based paint
on a bioresorbable cellulose-based substrate
79 × 8−0.510.2–12.7[159]
Sustainable conductive ink on cellulose-based substrate36 × 1201.7-[161]
Paper substrate101.2 × 10.52.756.88[162]
Paper substrate92.4 × 103.19.22[162]
Table 6. Key parameters of most used RFID printing technologies [168].
Table 6. Key parameters of most used RFID printing technologies [168].
TechnologyInk Viscosity
(cP)
Line Width
(μm)
Layer Thickness
(μm)
Speed
(m/s)
Inkjet10–3030–501Slow
Flexo50–50050–100<1~8
Screen500–500030–505–100~1
EHD1–15,0000.1<1slow
AJ1–10005<1slow
Table 7. Examples of sustainable sensors for IoT.
Table 7. Examples of sustainable sensors for IoT.
SensingMaterialMain CharacteristicsBending CyclesRef.
Strain Gauge Factor
Starch, porous carbon134.2>1000[194]
Paper/MXene/sizing agent (PMS)161 (bending angle of 0–120°)100,000 (bending deformation of 30°)[174]
Starch, egg white, Ag->1000[195]
Candle soot (CS) particles, chitosan, potato starch (PS), polyvinyl alcohol (PVA), Fe3+ ions1.49 at 0 to 60% strain; 2.71 at 60–100% strain>1000[196]
Graphite powder and cellulose fibers from waste printing papers271000[197]
Pressure Pressure range and/or sensitivity
Polylactic acid piezoelectric film (DS-PLA)0.03–62 kPa1.08 million at a pressure of 4.3 kPa[178]
Starch, porous carbon0–250 kPa>1000[194]
Poly(ether carbonate)-based Polyurethane0.62–62.5 kPa6250[198]
PPy, paper4.8 kPa−1 at < 5.5 kPa, 1.7 kPa−1 at 5.5–40 kPa3D[176]
Polyaniline, silk fibroin, poly (lactic-co-glycolic acid), K-carrageenan165.3 kPa, 2.54 kPa−1>2000[183]
PDA–CCFN0 to 50 kPa1000, by repeatedly loading and unloading a pressure of 20 kPa[185]
AgCNT@textile-PDMS0.02 kPa−1 and 0.004 kPa−1 in the low-pressure (<11.67 kPa) and high-pressure (~11.67–33.3 kPa)-[186]
Humidity RH linear range and/or sensitivity
PEDOT:PSS electrode, CNF film20% to 85%RH-[199]
Graphene inks30%RH to 90%RH linear range; 0.55/%RH at 25 °C-[179]
E-PNs, G. sulfurreducens, Au electrodes, PI substrate20% to 95%RH; >6% relative conductance change per 1% RH change>1000 bending cycles[179]
Cellulose nanofiber/graphene nanoplatelet30%RH to 90%RH>1000 bending cycles[200]
Common kitchen salt (NaCl)40% RH up to 85% RH-[201]
Sensitivity
H2O2,
glucose
BGC printed inks27.25 μA mM−1 cm−270 cycles by 10% stretching and 1800 consecutive 90° bending cycles[180]
H2 gasChitosan/polyvinylpyrrolidone (CHP) polymeric substrate; ZnO thin film24% and 46% towards 0.5% and 2% H2-[202]
NO2 gasDried mango peel, graphene, ZnO and carbon nanotubesΔR/R0 = 0.21 at 100 ppm and RH = 35%300[203]
CO2 gasp(D-co-M)104–106 ppm detection range-[204]
NH3Cotton fibers/polyaniline (PANI)100 ppm NH3 Stability in bending from 0° to 60°[205]
Table 8. Principal substrate materials for sustainable flexible IoT.
Table 8. Principal substrate materials for sustainable flexible IoT.
SubstrateMaterialsBiodegradableRecyclable
Inorganic MaterialsCarbon YesYes
MagneticYes, except for ceramics Not always,
but reusable
Metals (thin foils)Mo, Fe, W, or ZnNot always,
but reusable
Organic MaterialsPolymersYesYes
TextilesYesYes
SilkYesYes
Paper and cellulose-basedYesYes
Table 9. Representative examples of flexible, eco-friendly RRAM.
Table 9. Representative examples of flexible, eco-friendly RRAM.
MaterialOn/Off
Current Ratio
Operation
Voltage
(V)
Data Retention Time
(s)
Pros for SustainabilityRef.
Al/gelatin/Ag sandwiched structure on a bio-cellulose film>104<37 × 103Fully biodegradable device[213]
Ag/pectin/FTO104<3108Biocompatibility derived from use of natural pectin[214]
Carbon dot (CD)-polyvinyl pyrrolidone (PVP) nanocomposite and a silver nanowire (Ag NW) network buried in a flexible gelatin film>102−1.12>104Fully biocompatible[215]
Au/starch/ITO/PET1030.25103Biocompatible materials[216]
Au/starch–chitosan/ITO/PET1000.25104Biocompatible materials[216]
Poly(ethylene furanoate) (PEF) as substrate; deoxyribonucleic acid (DNA) as active layer104−2104Biomaterials[217]
Iron (Fe) ions in gelatin matrixes on paper substrates105<4.27 × 104Gelatin materials are biodegradable and recyclable[218]
W/silk fibroin/Mg1052.0-Good biodegradability[219]
Egg protein and graphene quantum dot composites1.19 × 1040.3104Good biodegradability[220]
Table 10. Representative examples of flexible energy harvesting devices.
Table 10. Representative examples of flexible energy harvesting devices.
Energy SourceType of EHMaterialOutput ***Pros for SustainabilityRef.
Body motionPENG *ZnO nanorods on the
surface of paper
Vo = 15 mV; Io = 10 nACost-effective; paper substrate[226]
MechanicalPENG Lead-free organic inorganic hybrid perovskiteVo = 94.5 Vpp, Io = 19.1 μApp; output power density of 18.95 μW/cm2Lead-free[227]
Body motionPENGZnOVo = 15 mVEco-friendly, low temperature and low-cost process[228]
Body motionTENG **Worn-out textiles from the waste binVo = 4.2 V; Io = 2.7 nAPromote the eco-friendly concept of recycling, reuse[230]
WindTENGRabbit furFor wind speed of 6 m/s, peak power = 11.9 mW; conversion efficiency of 15.4%Smart-farming applications without environment deterioration[232]
MechanicalTENGPolyvinyl butyral
(PVB); indium oxide (IO); Mylar
Vo = 700 V; Io = 1.52 mA/m2Energy-saving[233]
Wind, MechanicalTENGNatural leaf as an electrification layer and electrodeP ≈45 mW m−2Natural materials[232]
WindTENGPlant leaf and leaf powderIo = 60 μA; Vo = 1000 VNatural materials[232]
VibrationalTENGCellulose acetate nanofibers (CANF) and micro-patterned PDMSVo = 400 V; Io = mA/m2Cellulose-based; biocompatible and biodegradable material[237]
MechanicalPTENGMoS2-PVDFVo = 35.3 VEnergy saving for smart wearable devices[234]
BiomechanicalPTENGBi4Ti3O12 (BiTO)/polydimethylsiloxane (PDMS)Vo = 300 V; Io = 4.7 μASimple and cost-effective fabrication technique[235]
Hand clappingPTENGPVDF; Textured PDMS and skinVo = 750 V, Io = 400 μAHuman skin-based; can promote additional health benefit for people[238]
Waste heat energyThermoelectricSilicon rubber sheet, electrodeposited n-type thermoelectric materialVo = 1 V under a temperature difference T of 60 °C.Mountable on complex geometries for powering wireless IoT sensing systems in smart agriculture, smart home, industry application, and environment monitoring[239]
ThermalThermoelectricOrigami and kirigami-enabled resorbable TE paper, with a self-assembled inorganic particle network layer below the cellulose polymer bio-matrix layerVo = 38.55 mV, Io = 12.14 μA for a temperature difference of 24 KSignificant implications in the field of green technology; completely decomposed without carbon emission in water[240]
RF and solarRectenna; solar cellTi and Au on PDMS; amorphous silicon2613.6 μW in sunny outdoor. Additional 3.3–37.5% hybrid output dc-power when the RF source power is varied from 9 to 14 dBmEnergy saving: high efficiency[243]
* PENG: Piezoelectric Nanogenerator. ** TENG: Triboelectric Nanogenerator. *** Output voltage (Vo), output current (Io), output power or conversion efficiency are reported are reported according to the availability of data in the references.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Scandurra, G.; Arena, A.; Ciofi, C. A Brief Review on Flexible Electronics for IoT: Solutions for Sustainability and New Perspectives for Designers. Sensors 2023, 23, 5264. https://doi.org/10.3390/s23115264

AMA Style

Scandurra G, Arena A, Ciofi C. A Brief Review on Flexible Electronics for IoT: Solutions for Sustainability and New Perspectives for Designers. Sensors. 2023; 23(11):5264. https://doi.org/10.3390/s23115264

Chicago/Turabian Style

Scandurra, Graziella, Antonella Arena, and Carmine Ciofi. 2023. "A Brief Review on Flexible Electronics for IoT: Solutions for Sustainability and New Perspectives for Designers" Sensors 23, no. 11: 5264. https://doi.org/10.3390/s23115264

APA Style

Scandurra, G., Arena, A., & Ciofi, C. (2023). A Brief Review on Flexible Electronics for IoT: Solutions for Sustainability and New Perspectives for Designers. Sensors, 23(11), 5264. https://doi.org/10.3390/s23115264

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop