Search Results (4,031)

Gallium oxide (Ga₂O₃)-based memristors are gaining traction as promising candidates for next-generation electronic devices toward in-memory computing, leveraging the unique properties of Ga₂O₃, such as its wide bandgap, high thermodynamic stability, and chemical stability. This review explores the evolution of memristor theory for Ga₂O₃-based materials, emphasising capacitive memristors and their ability to integrate resistive and capacitive switching mechanisms for multifunctional performance. We discussed the state-of-the-art fabrication methods, material engineering strategies, and the current challenges of Ga₂O₃-based memristors. The review also highlights the applications of these memristors in memory technologies, neuromorphic computing, and sensors, showcasing their potential to revolutionise emerging electronics. Special focus has been placed on the use of Ga₂O₃ in capacitive memristors, where their properties enable improved switching speed, endurance, and stability. In this paper we provide a comprehensive overview of the advancements in Ga₂O₃-based memristors and outline pathways for future research in this rapidly evolving field. Full article

This Special Issue based on 15 articles/reviews focusses on low-cost sensor technology, gas sensors, chemical sensors, advanced active materials, sensing nanomaterials, sensor nodes, hardware innovation, data communication, system integration, sensor testing, functional characterization, sensor modeling, processing and correction algorithms, new sensing solutions, advanced proof of concepts, and chemical detection applications. Proper calibration techniques of chemical sensors have been explored, both in the laboratory and in field applications. Sensing solutions have been applied in the context of biochemical detection and gas monitoring. Full article

Lubricating oils are utilised in equipment and machinery to reduce friction and enhance material utilisation. The utilisation of oil leads to an increase in its thickness and density over time. Current methods for assessing oil life are slow, expensive, and complex, and often only applicable in laboratory settings and unsuitable for real-time or field use. This leads to unexpected equipment failures, unnecessary oil changes, and economic and environmental losses. A comprehensive review of the extant literature revealed no studies and no national or international patents on neural network algorithm-based oil life modelling and classification using green sensors. In order to address this research gap, this study, for the first time in the literature, provides a green conductivity sensor with high-accuracy prediction of oil life by integrating real-time field measurements and artificial neural networks. This design is based on analysing resistance change using a relatively low-cost, three-dimensional, eco-friendly sensor. The sensor is characterised by its simplicity, speed, precision, instantaneous measurement capability, and user-friendliness. The MLP and LVQ algorithms took as input the resistance values measured in two different oil types (diesel, bench oil) after 5–30 h of use. Depending on their degradation levels, they classified the oils as ‘diesel’ or ‘bench oil’ with 99.77% and 100% accuracy. This study encompasses a sensing system with a sensitivity of 50 µS/cm, demonstrating the proposed methodologies’ efficacy. A next-generation decision support system that will perform oil life determination in real time and with excellent efficiency has been introduced into the literature. The components of the sensor structure under scrutiny in this study are conducive to the creation of zero waste, in addition to being environmentally friendly and biocompatible. The developed three-dimensional green sensor simultaneously detects physical (resistance change) and chemical (oxidation-induced polar group formation) degradation by measuring oil conductivity and resistance changes. Measurements were conducted on simulated contaminated samples in a laboratory environment and on real diesel, gasoline, and industrial oil samples. Thanks to its simplicity, rapid applicability, and low cost, the proposed method enables real-time data collection and decision-making in industrial maintenance processes, contributing to the development of predictive maintenance strategies. It also supports environmental sustainability by preventing unnecessary oil changes and reducing waste. Full article

Aminoglycoside antibiotics (AGs) are widely used in medicine and animal husbandry, but they pose significant risks due to residual toxicity and antibiotic resistance. In this study, a novel chemical sensor based on the laccase-like activity of CH₃COOAg was developed for the selective detection of AGs. CH₃COOAg exhibited varying degrees of laccase-like activity in different buffers (MES, HEPES, and NaAc) and H₂O, and five AGs showed distinct intensities of the inhibitory effect on the laccase-like activity of CH₃COOA in different buffers and H₂O. Therefore, a four-channel colorimetric sensor array was constructed in combination with the use of principal component analysis (PCA) and Hierarchical Cluster Analysis (HCA) for the efficient identification of five AGs (0.02–0.3 μM) in environment samples like tap and lake water. In addition, a colorimetric method was developed for kanamycin (KAN) detection in a honey sample with a linear range of 10–100 nM (R² = 0.9977). The method has excellent sensitivity with a limit of detection of 3.99 nM for KAN. This work not only provides a rapid and low-cost detection method for AG monitoring but also provides a reference for the design of non-copper laccase mimics. Full article

The increasing presence of microplastics (MPs) and nanoplastics (NPs) in environmental matrices presents substantial analytical challenges due to their small size and chemical diversity. This study introduces a novel enzymatic biosensor based on the Surface Plasmon Resonance (SPR) platform for the sensitive detection of MPs and NPs, utilizing laccase as the recognition element. Standard plastic particles, including polystyrene (PS, 0.1 µm), polymethyl methacrylate (PMMA, 1.0 µm and 100 µm), and polyethylene (PE, 34–50 µm), were analyzed using SPR angular interrogation along with a fixed-angle scheme. The angular approach revealed a clear relationship between the resonance angle, particle size, and refractive index, while the fixed-angle method, combined with immobilized laccase, facilitated specific detection through enzyme/substrate interactions. The analytical parameters showed detection limits ranging from 7.5 × 10⁻⁴ µg/mL (PE, 34–50 µm) to 253.2 µg/mL (PMMA, 1 µm), with significant differences based on polymer type and enzymatic affinity. Application of the biosensor to real rainwater samples collected from two regions in Mexico (Tula and Molango) confirmed its functionality, although performance varied depending on matrix composition, exhibiting inhibition in samples with high manganese (Mn²⁺), chromium (Cr²⁺), and zinc (Zn²⁺) content. Despite these limitations, the sensor achieved a 113% recovery rate in Tula rainwater, demonstrating its potential for straightforward in situ environmental monitoring. This study highlights the capabilities of laccase-based SPR biosensors in enhancing microplastic detection and underscores the necessity of considering matrix effects for real-world applications. Full article

This study presents a comprehensive analysis aimed at validating the use of an innovative nanosensor based on graphitic nanomembranes for the smart monitoring of industrial wastewater. The validation of the potential of the nanosensor was carried out through the development of advanced analytical methodologies, a direct experimental comparison with commercially available electrode sensors commonly used for the detection of chemical species, and the evaluation of performance under conditions very similar to real-world field applications. The investigation involved a series of controlled experiments using an organic pollutant—benzoquinone—at varying concentrations. Initially, data analysis was performed using classical linear regression models, representing a conventional approach in chemical analysis. Subsequently, a more advanced methodology was implemented, incorporating machine-learning techniques to train a classifier capable of detecting the presence of pollutants in water samples. The study builds upon an experimental protocol previously developed by the authors for the nanomembranes, based on electrochemical impedance spectroscopy. The results clearly demonstrate that integrating the nanosensor with machine-learning algorithms yields significant performance. The intrinsic properties of the nanosensor make it well-suited for potential integration into field-deployable platforms, offering a real-time, cost-effective, and high-performance solution for the detection and quantification of contaminants in wastewater. These features position the nanomembrane-based sensor as a promising alternative to overcome current technological limitations in this domain. Full article

This review summarizes the recent advances in the application of nanomaterial coatings in optical fiber sensors, with a particular focus on deposition techniques and the research progress over the past five years in humidity sensing, gas detection, and biosensing. Benefiting from the high specific surface area, abundant surface active sites, and quantum confinement effects of nanomaterials, advanced thin-film fabrication techniques—including spin coating, dip coating, self-assembly, physical/chemical vapor deposition, atomic layer deposition (ALD), electrochemical deposition (ECD), electron beam evaporation (E-beam evaporation), pulsed laser deposition (PLD) and electrospinning, and other techniques—have been widely employed in the construction of functional layers for optical fiber sensors, significantly enhancing their sensitivity, response speed, and environmental stability. Studies have demonstrated that nanocoatings can achieve high-sensitivity detection of targets such as humidity, volatile organic compounds (VOCs), and biomarkers by enhancing evanescent field coupling and enabling optical effects such as surface plasmon resonance (SPR), localized surface plasmon resonance (LSPR), and lossy mode resonance (LMR). This paper first analyzes the principles and optimization strategies of nanocoating fabrication techniques, then explores the mechanisms by which nanomaterials enhance sensor performance across various application domains, and finally presents future research directions in material performance optimization, cost control, and the development of novel nanocomposites. These insights provide a theoretical foundation for the functional design and practical implementation of nanomaterial-based optical fiber sensors. Full article

Fentanyl and its analogues represent a severe threat due to their extreme potency and increasing prevalence in illicit drug supplies. Even trace amounts (on the order of a couple of milligrams) can be lethal, contributing to a surge in opioid overdose deaths worldwide. Beyond the public health crisis, fentanyl has emerged as a security concern, with the potential for deliberate use as a chemical agent in CBRN scenarios. This underscores the critical need for rapid and accurate detection methods that can be deployed by security forces and first responders. Modern technology offers a range of solutions—from portable mass spectrometers and spectroscopic devices to electrochemical sensors and immunoassay kits—that enable on-site identification of fentanyl and its analogues. This review provides a comprehensive overview of detection techniques, examining their capabilities and applications in law enforcement, border control, and CBRN incident response. We highlight how integration of advanced sensors with machine learning is enhancing detection accuracy in complex field environments. Challenges such as operational constraints and the ever-evolving variety of fentanyl analogues are discussed, and future directions are recommended to improve field-deployable detection tools for safety and security applications. Full article

Rapid on-site detection of chemical warfare agents (CWAs) is vital for security and environmental monitoring. In this work, copper(I) oxide (Cu₂O) nanowire (NW) chemiresistors were investigated as gas sensors for low-concentration organophosphorus chemical warfare agent (CWA) simulants. The NWs were hydrothermally synthesized and deposited onto microheater platforms, enabling them to operate at elevated working temperatures. Their sensing performance was tested against a range of vapor-phase simulants, including dimethyl methylphosphonate (DMMP), triethyl phosphate (TEP), diethyl ethylphosphonate (DEEP), diphenyl phosphoryl chloride (DPPCl), parathion, diethyl phosphite (DEP), diethyl adipate (DEA), and cyanogen chloride (ClCN). Fully oxidized P(V) simulants (DMMP, DEEP, TEP) produced modest, predominantly reversible responses (~3–6% RR). On the contrary, DPPCl and DEP induced the strongest relative responses (RR −94.67% and >200%, respectively), accompanied by irreversible surface modification as revealed by SEM and EDS. ClCN produced a substantial but reversible negative response (RR −9.5%), consistent with transient oxidative interactions. Surface poisoning was confirmed after exposure to DEP and DPPCl, which left phosphorus or chlorine residues on the Cu₂O surface. These results highlight both the promise and limitations of Cu₂O NW chemiresistors for selective CWA detection. Full article

Understanding mixing behavior at river confluences is essential for effective watershed management in response to increasing environmental issues such as algal blooms and chemical pollution. This study focused on the confluence of the Nakdong and Geumho Rivers, employing high-resolution field measurements using an ADCP (M9) and YSI EXO sensors. Water temperature (°C) and electrical conductivity (μS/cm) data were collected under three representative conditions, including flow ratios of 0.91, 0.45, and 0.29, as well as 0.05, with a maximum temperature difference of up to 6 °C. Mixing behavior was three-dimensionally analyzed by integrating cross-sectional and longitudinal data, and the accuracy of visualization was evaluated using IDW and Kriging spatial interpolation techniques. The analysis revealed that under low flow ratio conditions, vertical mixing was delayed; the thermal stratification persisted up to approximately 3 km downstream from the confluence (Line 3), and complete mixing was not achieved until about 7 km downstream (Line 5) due to density currents. Quantitative comparison indicated that IDW (R² = 0.901, RMSE = 31.522) outperformed Kriging (R² = 0.79, RMSE = 35.458). This study provides a quantitative criterion for identifying the mixing completion zone, thereby addressing the limitations of previous studies that relied on numerical models or limited field data, and offering practical evidence for water quality monitoring and sustainable river management. Full article

The shelf life of perishable foods is traditionally determined by microbiological, chemical, and sensory analyses, which are well-established and reliable. However, these methods can be time-consuming and resource-intensive, and they may not fully account for unexpected storage deviations, such as temperature fluctuations or equipment failures. Smart films emerge as a promising alternative, enabling rapid, visual, and low-cost food quality monitoring. This study developed smart films based on zein/gelatin/cellulose nanocrystals (Z/G/CNC) functionalized with alizarin (AL, 0–3% w/w), produced by casting (12.5% zein, 12.5% gelatin, and 5% CNC w/w). The films were characterized for morphological, physicochemical, thermal, and spectroscopic properties, chromatic response at pH 3–11, activity against Escherichia coli and Staphylococcus aureus, and applicability in monitoring Merluccid hake fillets. The incorporation of AL reduced water solubility, increased water vapor permeability and contact angle, imparted a more intense orange coloration, and improved thermal resistance. AL also increased thickness and elongation at break while reducing tensile strength and Young’s modulus. All films exhibited excellent UV-blocking capacity (<1% transmittance). Noticeable color changes were observed, with the Z/G/CNC/AL1 film being the most sensitive to pH variations. During Merluccid hake storage, ΔE values exceeded 3 within 72 h, with a color change from orange to purple, correlating with fillet pH (8.14) and total volatile basic nitrogen (TVB-N) (24.73 mg/100 g). These findings demonstrate the potential of the developed films as biodegradable sensors for smart packaging of perishable foods. Full article

Cognitive Odor Source Localization (OSL) strategies are reliable search strategies for turbulent environments, where chemical cues are sparse and intermittent. These methods estimate a probabilistic belief over the source location using Bayesian inference and guide the searching movement by evaluating expected entropy reduction at candidate new positions. By maximizing expected information gain, agents make informed decisions rather than simply reacting to sensor readings. However, computing entropy reductions is computationally expensive, making real-time implementation challenging for resource-constrained platforms. Interestingly, search trajectories produced by cognitive algorithms often resemble those of small insects, suggesting that informative movement patterns might be replicated using simpler, bio-inspired searching strategies. This work investigates that possibility by analysing spatial distribution of entropy reductions across the entire search area. Rather than focusing on searching algorithms and local decisions, the analysis maps information gain over the full environment, identifying consistent high-gain regions that may serve as navigational cues. Results show that these regions often emerge near the source and along plume borders and that expected entropy reduction is strongly influenced by prior belief shape and sensor observations. This global perspective enables identification of spatial patterns and high-gain regions that remain hidden when analysis is restricted to local neighborhoods. These insights enable synthesis of hybrid search strategies that preserve cognitive effectiveness while significantly reducing computational cost. Full article

The advancement of laser-induced graphene (LIG) has significantly enhanced the development of wearable and flexible electronic devices. Due to its exceptional physical, chemical, and electronic properties, LIG has emerged as a highly effective active material for wearable sensors. However, despite the wide range of materials suitable as precursors for LIG, the scarcity of stretchable and biocompatible polymers amenable to laser graphenization has remained a persistent challenge. In this study, laser-induced graphene (LIG) was fabricated directly on biocompatible and flexible cross-linked PDMS/PEG (with M_n (PEG) = 400 g/mol) composites for the first time, enabling their application in wearable sensors. The addition of PEG compensates for the low carbon content in PDMS, enabling efficient laser graphenization. Laser parameters were systematically optimized to achieve high-quality graphene, and a comprehensive characterization with varying PEG content (10–40 wt.%) was conducted using multiple analytical techniques. Tensile tests revealed that incorporating PEG significantly enhanced elongation at break, reaching 237% for PDMS/40 wt.% PEG while reducing Young’s modulus to 0.25 MPa, highlighting the excellent flexibility of the substrate material. Surface analysis using X-ray photoelectron spectroscopy (XPS), scanning electron microscopy (SEM), and Raman spectroscopy demonstrated the formation of high-quality few-layer graphene with the fewest defects in PDMS/40 wt.% PEG composites. Nevertheless, the adhesion of electrical contacts to LIG that was directly induced on PDMS/PEG proved to be challenging. To overcome this challenge, we produced devices by means of laser induction on polyimide and transfer to PDMS/PEG. We demonstrate the practical utility of such devices by applying them to monitor limb motion in real time. The sensor showed a stable and repeatable piezoresistive response under multiple bending cycles. These results provide valuable insights into the fabrication of biocompatible LIG-based flexible sensors, paving the way for their broader implementation in medical and sports technologies. Full article

Optical fiber-based biosensors have proven to be a powerful platform for chemical and biological analysis due to their compact size, fast response, high sensitivity, and immunity to electromagnetic interference. Among the various fiber designs, tapered optical fibers have gained prominence due to the increased evanescent fields that significantly improve light–analyte interactions, making them well-suited for advanced sensing applications. At the same time, advances in microfluidics have allowed for the precise control of small-volume fluids, supporting integration with optical fiber sensors to create compact and multifunctional optofluidic systems. This review explores recent developments in optical fiber optofluidic sensing, with a focus on two primary architectures: in-fiber and outside-fiber platforms. The advantages, limitations, and fabrication strategies for each are discussed, along with their compatibility with various sensing mechanisms. Special emphasis is placed on tapered optical fibers, focusing on design strategies, fabrication, and integration with microfluidics. While in-fiber systems offer compactness and extended interaction lengths, outside-fiber platforms offer greater mechanical stability, modularity, and ease of functionalization. The review highlights the growing interest in tapered fiber-based optofluidic biosensors and their potential to serve as the foundation for autonomous lab-on-a-fiber technologies. Future pathways for achieving self-contained, multiplexed, and reconfigurable sensing platforms are also discussed. Full article

Water quality monitoring and evaluation are essential across multiple sectors, including public health, environmental protection, agriculture and livestock management, industrial processes, and broader sustainability efforts. Conventional water analysis techniques, although accurate, are often constrained by their labor-intensive nature, extended processing times, and limited applicability for in situ, real-time monitoring. In recent years, spectroscopy-based methods have gained prominence as alternatives for water quality assessment, particularly when combined with chemometric analyses and advanced technological systems. This review provides an overview of the current advancements of spectroscopy-based water monitoring, with a focus on spectroscopy techniques operating within ultraviolet–visible (UV–Vis) and infrared (IR) spectral regions, which are currently applied for the assessment of a broad range of physicochemical and biological parameters relevant to livestock water management, including chemical oxygen demand (COD), dissolved organic carbon (DOC), nitrates, microbial contamination, and heavy metal ions. The findings highlight the growing utility of spectroscopy as a reliable tool in water quality assessment (e.g., COD detection with R² = 0.86 and nitrate detection with R² = 0.95 compared to traditional methods) and underpin the need for continued research into scalable, sensor-integrated solutions tailored for use in livestock farming environments. Full article