The Application of Nanosensors in Energy and Environment

A special issue of Nanomaterials (ISSN 2079-4991). This special issue belongs to the section "Nanoelectronics, Nanosensors and Devices".

Deadline for manuscript submissions: closed (10 October 2024) | Viewed by 5420

Special Issue Editor


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Guest Editor
Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing, China
Interests: iontronics; triboiontronics; nanoenergy; contact-electro-chemistry; nanosensor

Special Issue Information

Dear Colleagues,

Information flow in the central nervous system consumes little energy; the human brain, as a typical highly integrated iontronic center processing unit enabled by nanoconfined quantum superfluid, consumes only about 12 W. From the ubiquitous Internet of Things (IoTs) to highly integrated human brains, energy efficiency plays a critical role in the flow of information. Nanomaterials, nanostructures and nanointerfaces play essential roles in highly efficient energy and information flow, offering possibilities for novel sensors.

The present Special Issue of Nanomaterials presents the current state of the art in the use of nanomaterials, nanostructures, or nanointerfaces to generate new sensors, including approach perception sensors, gesture sensors, touch sensors, electronic tongues, self-powered sensors, etc. We also welcome work on new nanomaterials and nanostructures, for example nanofluidic materials and iontronic materials, for novel sensing technology and systems.

We invite contributions from leading groups in the field with the aim of giving a balanced view of the current state of the art in this discipline.

Prof. Dr. Di Wei
Guest Editor

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Keywords

  • nanomaterials
  • nanotechnology
  • energy
  • environment
  • sensors

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Published Papers (3 papers)

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Research

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14 pages, 5413 KiB  
Article
A Bulk Oxygen Vacancy Dominating WO3−x Photocatalyst for Carbamazepine Degradation
by Weiqing Guo, Qianhui Wei, Gangrong Li, Feng Wei and Zhuofeng Hu
Nanomaterials 2024, 14(11), 923; https://doi.org/10.3390/nano14110923 - 24 May 2024
Cited by 1 | Viewed by 746
Abstract
Creating oxygen vacancy in tungsten trioxide (WO3) has been considered as an effective strategy to improve the photocatalytic performance for degrading organic pollutants. In this study, oxygen vacancies were introduced into WO3 by thermal treatment under Ar atmosphere and their [...] Read more.
Creating oxygen vacancy in tungsten trioxide (WO3) has been considered as an effective strategy to improve the photocatalytic performance for degrading organic pollutants. In this study, oxygen vacancies were introduced into WO3 by thermal treatment under Ar atmosphere and their proportion was changed by setting different treatment times. WO3−x samples show better photoelectric properties and photocatalytic degradation performance for carbamazepine (CBZ) than an oxygen-vacancy-free sample, and WO3−x with the optimal proportion of oxygen vacancies is obtained by thermal treatment for 3 h in 550 °C. Furthermore, it discovers that the surface oxygen vacancies on WO3−x would be recovered when it is exposed to air, resulting in a bulk oxygen vacancy dominating WO3−x (bulk-WO3−x). The bulk-WO3−x exhibited much higher degradation efficiency for CBZ than WO3−x with both surface and bulk oxygen vacancies. The mechanism study shows bulk-WO3−x mainly degrades the CBZ by producing OH radicals and superoxide radicals, while oxygen-vacancy-free sample mainly oxidizes the CBZ by the photoexcited hole, which requires the CBZ to be adsorbed on the surface for degradation. The radical generated by bulk-WO3−x exhibits stronger oxidizing capacity by migrating to the solution for CBZ degradation. In summary, the influence of oxygen vacancy on photocatalytic degradation performance depends on both the proportion and location distribution and could lie in the optimization of the photodegradation mechanism. The results of this study could potentially broaden our understanding of the role of oxygen vacancies and provide optimal directions and methods for oxygen vacancy regulation for photocatalysts. Full article
(This article belongs to the Special Issue The Application of Nanosensors in Energy and Environment)
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12 pages, 6986 KiB  
Article
WO3 Nanoplates Decorated with Au and SnO2 Nanoparticles for Real-Time Detection of Foodborne Pathogens
by Xueyan Li, Zeyi Wu, Xiangyu Song, Denghua Li, Jiajia Liu and Jiatao Zhang
Nanomaterials 2024, 14(8), 719; https://doi.org/10.3390/nano14080719 - 19 Apr 2024
Cited by 2 | Viewed by 1241
Abstract
Nowadays, metal oxide semiconductor gas sensors have diverse applications ranging from human health to smart agriculture with the development of Internet of Things (IoT) technologies. However, high operating temperatures and an unsatisfactory detection capability (high sensitivity, fast response/recovery speed, etc.) hinder their integration [...] Read more.
Nowadays, metal oxide semiconductor gas sensors have diverse applications ranging from human health to smart agriculture with the development of Internet of Things (IoT) technologies. However, high operating temperatures and an unsatisfactory detection capability (high sensitivity, fast response/recovery speed, etc.) hinder their integration into the IoT. Herein, a ternary heterostructure was prepared by decorating WO3 nanoplates with Au and SnO2 nanoparticles through a facial photochemical deposition method. This was employed as a sensing material for 3-hydroxy-2-butanone (3H-2B), a biomarker of Listeria monocytogenes. These Au/SnO2–WO3 nanoplate-based sensors exhibited an excellent response (Ra/Rg = 662) to 25 ppm 3H-2B, which was 24 times higher than that of pure WO3 nanoplates at 140 °C. Moreover, the 3H-2B sensor showed an ultrafast response and recovery speed to 25 ppm 3H-2B as well as high selectivity. These excellent sensing performances could be attributed to the rich Au/SnO2–WO3 active interfaces and the excellent transport of carriers in nanoplates. Furthermore, a wireless portable gas sensor equipped with the Au/SnO2–WO3 nanoplates was assembled, which was tested using 3H-2B with known concentrations to study the possibilities of real-time gas monitoring in food quality and safety. Full article
(This article belongs to the Special Issue The Application of Nanosensors in Energy and Environment)
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Review

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30 pages, 18195 KiB  
Review
Synergizing Machine Learning Algorithm with Triboelectric Nanogenerators for Advanced Self-Powered Sensing Systems
by Roujuan Li, Di Wei and Zhonglin Wang
Nanomaterials 2024, 14(2), 165; https://doi.org/10.3390/nano14020165 - 12 Jan 2024
Cited by 5 | Viewed by 2908
Abstract
The advancement of the Internet of Things (IoT) has increased the demand for large-scale intelligent sensing systems. The periodic replacement of power sources for ubiquitous sensing systems leads to significant resource waste and environmental pollution. Human staffing costs associated with replacement also increase [...] Read more.
The advancement of the Internet of Things (IoT) has increased the demand for large-scale intelligent sensing systems. The periodic replacement of power sources for ubiquitous sensing systems leads to significant resource waste and environmental pollution. Human staffing costs associated with replacement also increase the economic burden. The triboelectric nanogenerators (TENGs) provide both an energy harvesting scheme and the possibility of self-powered sensing. Based on contact electrification from different materials, TENGs provide a rich material selection to collect complex and diverse data. As the data collected by TENGs become increasingly numerous and complex, different approaches to machine learning (ML) and deep learning (DL) algorithms have been proposed to efficiently process output signals. In this paper, the latest advances in ML algorithms assisting solid–solid TENG and liquid–solid TENG sensors are reviewed based on the sample size and complexity of the data. The pros and cons of various algorithms are analyzed and application scenarios of various TENG sensing systems are presented. The prospects of synergizing hardware (TENG sensors) with software (ML algorithms) in a complex environment and their main challenges for future developments are discussed. Full article
(This article belongs to the Special Issue The Application of Nanosensors in Energy and Environment)
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