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Exploration of Advanced Applications of Triboelectric Nanogenerator-Based Self-Powered sensors in the Era of Artificial Intelligence

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sensors 2025年第8期25卷 2520-2520页

作者： Su, Yifeng Yin, Dezhi Zhao, Xinmao Hu, Tong Liu, Long Northwestern Polytech Univ Shenzhen Res & Dev Inst Shenzhen 518063 Peoples R China Northwestern Polytech Univ Sch Elect & Informat Xian 710072 Peoples R China

The integration of Deep Learning with sensor technologies has significantly advanced the field of intelligent sensing and decision making by enhancing perceptual capabilities and delivering sophisticated data analysis and processing functionalities. This review provides a comprehensive overview of the synergy between Deep Learning and sensors, with a particular focus on the applications of triboelectric nanogenerator (TENG)-based self-powered sensors combined with artificial intelligence (AI) algorithms. First, the evolution of Deep Learning is reviewed, highlighting the advantages, limitations, and application domains of several classical models. Next, the innovative applications of intelligent sensors in autonomous driving, wearable devices, and the Industrial Internet of Things (IIoT) are discussed, emphasizing the critical role of neural networks in enhancing sensor precision and intelligent processing capabilities. The review then delves into TENG-based self-powered sensors, introducing their self-powered mechanisms based on contact electrification and electrostatic induction, material selection strategies, novel structural designs, and efficient energy conversion methods. The integration of TENG-based self-powered sensors with Deep Learning algorithms is showcased through their groundbreaking applications in motion recognition, smart healthcare, smart homes, and human-machine interaction. Finally, future research directions are outlined, including multimodal data fusion, edge computing integration, and brain-inspired neuromorphic computing, to expand the application of self-powered sensors in robotics, space exploration, and other high-tech fields. This review offers theoretical and technical insights into the collaborative innovation of Deep Learning and self-powered sensor technologies, paving the way for the development of next-generation intelligent systems.

关键词： deep learning triboelectric nanogenerator (TENG) self-powered sensors intelligent sensors human-machine interaction internet of things (IoT)

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Transparent Touch sensors on Wood: Sustainable Development With Aesthetic Integrity

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IEEE sensors JOURNAL 2024年第12期24卷 19813-19820页

作者： Rauter, Lukas Jose, Manoj Weiss, Stephanie Moser, Olivia Pandur, Stefan Kosel, Jurgen Silicon Austria Labs GmbH Sensor Syst Div A-9524 Villach Austria Kompetenzzentrum Holz GmbH 9300 St Veit Glan Austria F List GmbH A-2842 Koenigsberg Austria

Sustainability is becoming an increasingly crucial focus across various industries, reigniting interest in wood as a material. To enable its broader utility and smart applications, the integration of electronics and sensors into wooden surfaces is essential. Yet, preserving the optical integrity of elegant wood finishes is of paramount importance. This article presents the realization of a transparent touch sensor on recon veneers. The touch sensor relies on an interdigital capacitor (IDC), screen-printed using silver nanowire ink. Applying a protective coating onto the sensor prevents responses to ambient humidity. The temperature dependence of the sensor signal exhibits a contrasting trend compared to the touch response. The sensor demonstrates the highest touch sensitivity at a frequency of 2.57 MHz. A standard deviation of 0.7% was found, highlighting the sensor's precision. This research paves the way for seamlessly incorporating advanced electronics into wooden surfaces while maintaining their aesthetic appeal and functionality.

关键词： Functional printed electronics sensors sustainability wood

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Transdisciplinary Innovations in Athlete Health: 3D-Printable Wearable sensors for Health Monitoring and Sports Psychology

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sensors 2025年第5期25卷 1453-1453页

作者： Sekeroglu, Mustafa Onder Pekgor, Metin Algin, Aydolu Toros, Turhan Serin, Emre Uzun, Meliha Cerit, Gunay Onat, Tugba Ermis, Sermin Agrali Mus Alparslan Univ Fac Sport Sci TR-49100 Mus Turkiye Swinburne Univ Technol Dept Mech & Prod Design Engn Melbourne Vic 3122 Australia Akdeniz Univ Fac Med TR-07058 Antalya Turkiye Mersin Univ Fac Sport Sci Dept Coaching Educ TR-33110 Mersin Turkiye Sirnak Univ High Sch Phys Educ & Sports TR-73000 Sirnak Turkiye Hakkari Univ Fac Sport Sci TR-30000 Hakkari Turkiye Aydin Adnan Menderes Univ Fac Sport Sci TR-09010 Aydin Turkiye

The integration of 3D printing technology into wearable sensor systems has catalyzed a paradigm shift in sports psychology and athlete health monitoring by enabling real-time, personalized data collection on physiological and psychological states. In this study, not only is the technical potential of these advancements examined but their real-world applications in sports psychology are also critically assessed. While the existing research primarily focuses on sensor fabrication and data acquisition, a significant gap remains in the evaluation of their direct impact on decision-making processes in coaching, mental resilience, and long-term psychological adaptation in athletes. A critical analysis of the current state of 3D-printed wearable sensors is conducted, highlighting both their advantages and limitations. By combining theoretical insights with practical considerations, a comprehensive framework is established for understanding how sensor-based interventions can be effectively incorporated into sports training and psychological evaluation. Future research should prioritize longitudinal studies, athlete-centered validation, and interdisciplinary collaborations to bridge the gap between technological developments and real-world applications. Additionally, the integration of artificial intelligence and advanced biomaterials has significant potential to enhance the reliability and interpretability of sensor-driven interventions. However, without rigorous scientific validation, their effectiveness remains uncertain. This study highlights the importance of a systematic approach in implementing and evaluating 3D-printed wearable sensors in sports psychology.

关键词： sports psychology wearable sensors 3D printing health monitoring athlete performance

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Development of electrochemical NH3 sensors with large sensitivity depending on the superposition enhancement effect of sensing electrodes

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CHEMICAL ENGINEERING JOURNAL 2025年 507卷

作者： Xu, Xiaodi Meng, Weiwei Kong, Fanbo Yan, Bo Zheng, Shijie Li, Xu Zhu, Jing He, Zhangxing Li, Yuehua Meng, Wei Dai, Lei Wang, Ling North China Univ Sci & Technol Coll Mat Sci & Engn Tangshan 063009 Peoples R China Beijing Inst Technol Inst Adv Struct Technol Beijing 100081 Peoples R China North China Univ Sci & Technol Coll Met & Energy Tangshan 063009 Peoples R China North China Univ Sci & Technol Coll Chem Engn Tangshan 063009 Peoples R China

The application of electrochemical gas sensors with large sensitivity significantly reduces pollutant emissions and improves safety in the chemical industry, mining, metallurgy and energy storage fields. However, the benefits that originate from adopting novel sensing electrodes (SE) to improve the sensitivity of sensors are reducing due to the thermodynamic and kinetic limitations of these SEs. Thus, to efficiently utilize the developed SE, a generic method based on the superposition enhancement effect between the response signals of SEs is proposed to improve the sensitivity of electrochemical NH3 sensor. The integration of InVO4 SE and La2Ag0.05NiO4+delta SE producing opposite response signals leads to the sensitivity of IVO-LANO sensor significantly increased with 85.3 % and 137.6 % compared to InVO4 sensor and La2Ag0.05NiO4+delta sensor respectively, and the InVO4- La2Ag0.05- NiO4+delta (IVO-LANO) sensor exhibits largest sensitivity of 111.2 mV/decade at 500 degrees C. Quantitative analyses show that the acceleration of electrochemical reaction on the SE enhances the response signal of sensor, e.g., the increase in exchange current of IVO-LANO sensor by 1 mu A/cm-2 leads to the increase in response signal by 508 mV. Benefiting from small gas diffusion resistance and the adopted high-performance SEs, the IVO-LANO sensor shows short response times, good selectivity and reproducibility. The integration of two SEs producing opposite signals shows good prospects for the development of electrochemical gas sensors with large sensitivity.

关键词： Electrochemical gas sensors Sensing electrode Superposition enhancement effect Sensitivity NH3

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Performance evaluation of embedded acoustic emission sensors in a glass fibre composite under mechanical loading

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APPLIED ACOUSTICS 2024年 218卷

作者： Ghadarah, Noor Salam Ayre, David Cranfield Univ Sch Aerosp Transport & Mfg Bedford MK43 0AL England

Fully embedding acoustic emission (AE) sensors in a composite is postulated to increase the sensor's sensitivity. The increased sensitivity will enable the sensor to detect subtle structural changes and lower the number of sensors required for monitoring. However, researchers have differed on whether the sensitivity increases or not. To robustly investigate the fully embedded AE sensor's sensitivity, the surface and fully embedded AE sensors are placed on the same specimen, away from the high-stress region and compared based on the same hit. Two mechanical testing methods are used: three-point bend and double cantilever beam (DCB) mode I. Three sensors are embedded: lead zirconate titanate (PZT), gold-plated polyvinylidene difluoride (PVDF), silver-ink PVDF. The DCB mode I method showed that the sensitivity of the surface embedded sensor was generally better or equal to the fully embedded sensor. However, the three-point bend method showed similar sensitivity with both surface and fully embedded sensors. The results indicate that the fully embedded sensor's sensitivity is based on the frequency of the AE event and, therefore, the failure type. The aim of the study is to analyse the sensitivity of the surface-embedded and fully embedded sensors concerning surface-originating and subsurface-originating cracks.

关键词： AE PZT PVDF integration Sensitivity

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integration of Exfoliated WS2/Functionalized MWCNT Nanocomposites for NO2 Sensing Using Machine Learning for Response Prediction

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IEEE sensors JOURNAL 2024年第22期24卷 36366-36376页

作者： Kumar, Sunil Kedam, Naresh Maksimovskiy, Evgeny A. Ishchenko, Arcady V. Larina, Tatyana V. Chesalov, Yuriy A. Bannov, Alexander G. Chinese Acad Sci Anhui Inst Opt & Fine Mech Hefei Inst Phys Sci Anhui Prov Key Lab Photon Devices & Mat Hefei Peoples R China Chinese Acad Sci Hefei Inst Phys Sci Key Lab Photovolta & Energy Conservat Mat Hefei Peoples R China Samara Natl Res Univ Dept Thermal Engn & Thermal Engines Samara Russia RAS Inst Inorgan Chem SB Novosibirsk Russia RAS Boreskov Inst Catalysis SB Novosibirsk Russia Novosibirsk State Tech Univ Dept Chem & Chem Engn Novosibirsk Russia

In order to manage the environment and perform noninvasive disease diagnostics, it is necessary to continuously identify harmful and highly toxic gases, such as nitrogen dioxide (NO2). This study demonstrates how to design nanocomposites and build a cost-effective NO2 gas sensor based on exfoliated tungsten disulphide and functionalized multiwalled carbon nanotubes (f-MWCNTs) as a highly efficient sensing material operating at room temperature (RT) in humid conditions. The composite sensor's response under various humidity levels, ranging from 2% to 65%, as well as at different temperatures ( 25 C-degrees- 80( degrees)C), was studied. Scanning electron microscopy (SEM), Raman spectroscopy, transmission electron microscopy (TEM), and energy-dispersive X-ray spectroscopy (EDX) were used to analyze the sensing material. The composite-based sensor showed an improved response Delta R/R0 of 52% at RT for 50-ppm NO2 with good selectivity to other gases (e.g., ammonia, methane, benzene, isobutene, and hydrogen). The composite sensor exhibited a low detection limit of 1.39 ppm for NO2 at RT. Furthering this advancement, we delve into the integration of machine learning, specifically the CatBoost regression model, with the NO(2 )sensor. This integration elevates the sensor from a conventional passive detector to an advanced analytical system, significantly boosting its predictive accuracy and adaptability for real-time environmental monitoring and nuanced data interpretation, thereby opening new frontiers in sensor technology and applications in environmental monitoring and health diagnostics.

关键词： Carbon nanotubes Carbon nanotubes gas sensors gas sensors nitrogen dioxide (NO2) regression regression

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Miniaturized Plasmonic sensor With Dual-Function Capability for Pressure and Flow Rate Detection at Subwavelength Levels

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IEEE sensors JOURNAL 2025年第4期25卷 6176-6182页

作者： Rahad, Rummanur Das Joy, Joyonta Rahman, Md. Shakibur Emon, Md Jahidul Hoq Haque, Mohammad Ashraful Rice Univ Dept Elect & Comp Engn Houston TX 77005 USA Chittagong Univ Engn & Technol Dept Elect & Elect Engn Chittagong 4349 Bangladesh Islamic Univ Technol Dept Elect & Elect Engn Gazipur 1704 Bangladesh

This article introduces a nanoscale plasmonic sensor with dual-function capabilities for detecting both pressure and flow rate, designed using a metal-insulator-metal (MIM) bus waveguide coupled with a resonator featuring a horizontal slot and multiple stubs. The sensor demonstrates high pressure sensitivity, achieving 1100.70 nm/MPa for pressure detection, with a figure of merit (FOM) of 4.6054, and effectively measures flow rates from 58.544 to 356.43 pL/s using an optical spectrum analyzer (OSA). Finite-element method (FEM) simulations were employed to analyze the pressure-induced wavelength shifts, enhancing the sensor's versatility in integrated sensing applications. The sensor's compact footprint, simplicity, and ease of fabrication make it ideal for integration into laboratory-on-a-chip devices. Its dual functionality provides a novel solution for precise, real-time monitoring in biomedical and microfluidic engineering applications.

关键词： sensors Silver Sensitivity Optical resonators Plasmons Deformation Atmospheric modeling Biomedical optical imaging Pressure sensors Optical fiber sensors Finite-element method (FEM) flow rate detection metal-insulator-metal (MIM) plasmonic sensor pressure sensing surface plasmon polaritons (SPPs)

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Detection of PPB-Level H2S Concentrations in Exhaled Breath Using Au Nanosheet sensors with Small Variability, High Selectivity, and Long-Term Stability

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ACS sensors 2024年第2期9卷 708-716页

作者： Kato, Taro Tanaka, Takahisa Uchida, Ken Univ Tokyo Dept Mat Engn Tokyo 1138656 Japan

The continuous monitoring of hydrogen sulfide (H2S) in exhaled breath enables the detection of health issues such as halitosis and gastrointestinal problems. However, H2S sensors with high selectivity and parts per billion-level detection capability, which are essential for breath analysis, and facile fabrication processes for their integration with other devices are lacking. In this study, we demonstrated Au nanosheet H2S sensors with high selectivity, ppb-level detection capability, and high uniformity by optimizing their fabrication processes: (1) insertion of titanium nitride (TiN) as an adhesion layer to prevent Au agglomeration on the oxide substrate and (2) N-2 annealing to improve nanosheet crystallinity. The fabricated Au nanosheets successfully detected H2S at concentrations as low as 5.6 ppb, and the estimated limit of detection was 0.5 ppb, which is superior to that of the human nose (8-13 ppb). In addition, the sensors detected H2S in the exhaled breath of simulated patients at concentrations as low as 175 ppb while showing high selectivity against interfering molecules, such as H-2, alcohols, and humidity. Since Au nanosheets with uniform sensor characteristics enable easy device integration, the proposed sensor will be useful for facile health checkups based on breath analysis upon its integration into mobile devices.

关键词： gas sensor hydrogen sulfide gold nanosheet breath analysis ppb-level detection

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Temperature-Modulated UV-Enhanced SnO2 Gas sensor Cell With High-Stability and High-Selectivity Gas Discrimination

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IEEE sensors JOURNAL 2025年第1期25卷 244-252页

作者： Xing, Chong Xie, Dongcheng Zhang, Yan Zhang, Haochen Xu, Lei Wu, Feng Univ Sci & Technol China Sch Microelect Hefei 230027 Peoples R China Micro Nano Sensing Hefei Technol Co Ltd Hefei 230000 Peoples R China Univ Sci & Technol China Sch Informat Sci & Technol Hefei 230027 Peoples R China

This article presents a low-power, temperature-modulated gas sensor cell based on tin dioxide, aimed at improving the selectivity of single-material metal oxide gas sensors. This gas sensor cell features hexagonal-structured six channels sharing a heater and six pairs of detecting electrodes sharing a single ground pad, significantly enhancing device integration. The proposed gas sensor cell uses a coplanar design of the heater and detecting electrodes with differential temperature gradients across six detection channels. By precisely designing the temperature distribution across the active regions of the six channels, the selectivity of the tin dioxide sensor cell is greatly enhanced. Furthermore, periodic ultraviolet (UV) light illumination improves the sensor's response speed and achieves gas discrimination by analysis of the sensitivities instead of whole curves. With the trend toward highly integrated microelectromechanical system (MEMSs) sensor, temperature modulation and UV enhancement hold promise for portable sensor monitoring.

关键词： sensors Gas detectors Temperature sensors Electrodes Resistance Land surface temperature Gases Temperature distribution Silicon Modulation Gas sensor microelectromechanical system (MEMS) temperature modulation ultraviolet (UV) enhancement

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A Review of Advanced Reactive Materials in Fiber sensor Technology: Challenges and Requirements of Precise Humidity Control in Mechanochemical Treatment Process

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sensors AND MATERIALS 2025年第1期37卷 41-61页

作者： Riza, Muhammad Arif Go, Yun Ii Zaitizila, Ismail Kok, Soo Ping Heriot Watt Univ Malaysia 1 Sch Engn & Phys Sci Jalan Venna P5-2 Precinct 5 Putrajaya 62200 Malaysia

In this comprehensive review, we explore the intersection of reactive materials in fiber sensor technology and the critical role of humidity control in mechanochemical treatment processes. We examine recent advancements in embedded sensor technologies, focusing on their applications in various treatment processes across industries. We highlight the development of metal oxide nanomaterials as effective optoelectronic humidity sensors, discussing their fabrication, characterization, and operational principles. In this review, we also delve into the emerging field of wearable chemical sensors, emphasizing their potential in healthcare monitoring and environmental applications. Furthermore, we address the integration of embedded sensors in Industry 4.0 frameworks, illustrating their impact on factory automation, process optimization, and sustainable manufacturing. We underscore the importance of humidity control in mechanochemical treatments, linking it to the broader context of sensor technology applications. By unifying current research and identifying future trends, we provide valuable insights into the synergies between reactive materials, fiber sensor technology, and humidity control, offering a foundation for further innovations in treatment process enhancement and efficiency.

关键词： sensitive industry moisture temperature

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