In this letter, we propose a smart cushioning device (SCD) that integrates a self-folded corrugated structure with a passive wireless sensing mechanism. Using inkjet printing–based self-folding, highly reproducible corrugated geometries were formed from planar paper sheets in a simple and scalable manner. By increasing the number of layers and paper thickness, the SCD achieved a maximum load capacity of 39.0 N, exhibiting a 1014% improvement in load-bearing capability compared to a single-layer configuration. A planar spiral coil integrated within the structure enabled LC resonance wireless sensing of deformation, showing up to 51.5% inductance variation and corresponding resonance frequency shifts. The response remained stable after 1000 compression cycles, confirming high mechanical durability. In a load-position identification test, the device exhibited a frequency shift of Δf/f0 = 0.10, demonstrating high spatial sensitivity to localized deformation. Owing to its modular design, the proposed SCD allows flexible adjustment of sensor number and placement, offering a promising approach for real-time pressure-distribution sensing in evacuation shelters, nursing care, and smart furniture applications.
{"title":"Development of Layered Origami Smart Cushioning Device With Wireless Self-Inductive Sensors","authors":"Satoshi Motoyama;Hiroaki Minamide;Takuma Harada;Hiroki Shigemune","doi":"10.1109/LSENS.2025.3647532","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3647532","url":null,"abstract":"In this letter, we propose a smart cushioning device (SCD) that integrates a self-folded corrugated structure with a passive wireless sensing mechanism. Using inkjet printing–based self-folding, highly reproducible corrugated geometries were formed from planar paper sheets in a simple and scalable manner. By increasing the number of layers and paper thickness, the SCD achieved a maximum load capacity of 39.0 N, exhibiting a 1014% improvement in load-bearing capability compared to a single-layer configuration. A planar spiral coil integrated within the structure enabled <italic>LC</i> resonance wireless sensing of deformation, showing up to 51.5% inductance variation and corresponding resonance frequency shifts. The response remained stable after 1000 compression cycles, confirming high mechanical durability. In a load-position identification test, the device exhibited a frequency shift of Δf/f<sub>0</sub> = 0.10, demonstrating high spatial sensitivity to localized deformation. Owing to its modular design, the proposed SCD allows flexible adjustment of sensor number and placement, offering a promising approach for real-time pressure-distribution sensing in evacuation shelters, nursing care, and smart furniture applications.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"10 2","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2025-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11314661","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145929523","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This letter presents an adaptive digitizing front-end for thermistors that enables precise and linear temperature estimation. The proposed design utilizes an enhanced relaxation oscillator offering several key advantages: lead-wire resistance compensation, constant-current excitation, reduced self-heating error, linearized output across $120^circ text{C}$ temperature range, and compatibility with standard components. A novel multiregime operation intelligently reduces conversion time to meet demanding requirements in advanced applications. Both the circuit architecture and numerical optimization methodology are described. Experimental validation using a prototype with commercial thermistors demonstrates linear temperature estimation with 0.4% nonlinearity and conversion time under 20 ms, confirming the suitability of the proposed approach for high-performance temperature measurement in automotive and aerospace applications.
{"title":"Linear Multiregime Thermistor Digitizer Featuring Lead-Wire and Self-Heating Compensation","authors":"Sajeev Ramachandran;Anoop Chandrika Sreekantan;Roy Thankachan","doi":"10.1109/LSENS.2025.3647785","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3647785","url":null,"abstract":"This letter presents an adaptive digitizing front-end for thermistors that enables precise and linear temperature estimation. The proposed design utilizes an enhanced relaxation oscillator offering several key advantages: lead-wire resistance compensation, constant-current excitation, reduced self-heating error, linearized output across <inline-formula><tex-math>$120^circ text{C}$</tex-math></inline-formula> temperature range, and compatibility with standard components. A novel multiregime operation intelligently reduces conversion time to meet demanding requirements in advanced applications. Both the circuit architecture and numerical optimization methodology are described. Experimental validation using a prototype with commercial thermistors demonstrates linear temperature estimation with 0.4% nonlinearity and conversion time under 20 ms, confirming the suitability of the proposed approach for high-performance temperature measurement in automotive and aerospace applications.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"10 2","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2025-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145982379","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-23DOI: 10.1109/LSENS.2025.3647319
Monu Kumar;Varun Goel;Yogesh Kumar
This work reports a solution-processed Ag/ZnO nanorod-array/ZnO quantum-dot/indium tin oxide (ITO) Schottky photodiode engineered to mimic synaptic functions for neuromorphic optoelectronics. Barrier-height inhomogeneity (BHI) at the Ag/ZnO interface, along with oxygen adsorption–desorption processes, generates interfacial trap states that regulate charge trapping and release, enabling short-term memory (STM) and long-term memory (LTM) behaviors. Temperature-dependent I-V analysis reveals an increase in effective barrier height from 0.59 to 0.77 eV and a decrease in ideality factor from 3.328 to 2.80 in the temperature range from 303 to 423 K, confirming BHI-dominated transport. Optical pulse measurements demonstrate tunable synaptic plasticity, including enhanced STM at higher temperatures and LTM retention up to 647 s at room temperature. The results establish a temperature-modulated Schottky synapse capable of controllable neuromorphic photoresponses.
{"title":"Temperature-Modulated Short-Term and Long-Term Memory in Solution-Processed ZnO Schottky Synapses","authors":"Monu Kumar;Varun Goel;Yogesh Kumar","doi":"10.1109/LSENS.2025.3647319","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3647319","url":null,"abstract":"This work reports a solution-processed Ag/ZnO nanorod-array/ZnO quantum-dot/indium tin oxide (ITO) Schottky photodiode engineered to mimic synaptic functions for neuromorphic optoelectronics. Barrier-height inhomogeneity (BHI) at the Ag/ZnO interface, along with oxygen adsorption–desorption processes, generates interfacial trap states that regulate charge trapping and release, enabling short-term memory (STM) and long-term memory (LTM) behaviors. Temperature-dependent <italic>I-V</i> analysis reveals an increase in effective barrier height from 0.59 to 0.77 eV and a decrease in ideality factor from 3.328 to 2.80 in the temperature range from 303 to 423 K, confirming BHI-dominated transport. Optical pulse measurements demonstrate tunable synaptic plasticity, including enhanced STM at higher temperatures and LTM retention up to 647 s at room temperature. The results establish a temperature-modulated Schottky synapse capable of controllable neuromorphic photoresponses.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"10 2","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2025-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145929625","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-23DOI: 10.1109/LSENS.2025.3647147
Roland Ramm;Yang Li;Alexander Oberdörster;Stefan Heist;Peter Kühmstedt;Gunther Notni
Monocular depth estimation is a computer vision task in which a neural network is trained to estimate depth maps from given images. Recently, some estimators have reached remarkable results with potential to replace conventional 3-D sensors in certain applications. To investigate how they compare in terms of metrological performance, we applied the VDI/VDE 2634 evaluation guideline from the “Verein Deutscher Ingenieure e.V.” This guideline is used to specify the probing and length measurement errors of a 3-D sensor by capturing data from a calibrated ball bar specimen in different orientations within a predefined measurement volume. We evaluated three recent monocular depth estimators Depth Anything V2, Depth Pro, and UniDepthV2 in different settings, which achieved probing and length measurement errors below 10 % under optimal conditions. However, under nonoptimal conditions, each of the three depth estimators showed significant errors. Adding everyday objects into the image scenes improved the overall results. Our image collection, the MD-VDI2634 dataset, enables the investigation and comparison of depth estimators regarding their metrological performance.
单目深度估计是一项计算机视觉任务，其中训练神经网络从给定图像中估计深度图。最近，一些估计器已经取得了显著的成果，在某些应用中有可能取代传统的3d传感器。为了研究它们在计量性能方面的比较，我们应用了来自“Verein Deutscher Ingenieure e.v.”的VDI/VDE 2634评估指南。本指南用于通过在预定义的测量体积内从校准的球杆样品中捕获不同方向的数据来指定三维传感器的探测和长度测量误差。我们在不同的环境下评估了三种最新的单目深度估计器depth Anything V2、depth Pro和UniDepthV2，在最佳条件下，探测和长度测量误差低于10%。然而，在非最优条件下，三种深度估计器均存在显著误差。将日常物品添加到图像场景中可以改善整体效果。我们的图像收集，MD-VDI2634数据集，可以对深度估计器的计量性能进行调查和比较。
{"title":"Benchmarking AI-Based Monocular Depth Estimators in Terms of Their Metrological Potential Following 3-D Sensor Guideline VDI/VDE 2634","authors":"Roland Ramm;Yang Li;Alexander Oberdörster;Stefan Heist;Peter Kühmstedt;Gunther Notni","doi":"10.1109/LSENS.2025.3647147","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3647147","url":null,"abstract":"Monocular depth estimation is a computer vision task in which a neural network is trained to estimate depth maps from given images. Recently, some estimators have reached remarkable results with potential to replace conventional 3-D sensors in certain applications. To investigate how they compare in terms of metrological performance, we applied the VDI/VDE 2634 evaluation guideline from the “Verein Deutscher Ingenieure e.V.” This guideline is used to specify the probing and length measurement errors of a 3-D sensor by capturing data from a calibrated ball bar specimen in different orientations within a predefined measurement volume. We evaluated three recent monocular depth estimators Depth Anything V2, Depth Pro, and UniDepthV2 in different settings, which achieved probing and length measurement errors below 10 % under optimal conditions. However, under nonoptimal conditions, each of the three depth estimators showed significant errors. Adding everyday objects into the image scenes improved the overall results. Our image collection, the MD-VDI2634 dataset, enables the investigation and comparison of depth estimators regarding their metrological performance.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"10 2","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2025-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11313486","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145982343","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-23DOI: 10.1109/LSENS.2025.3646977
Junaid Ahmed Qureshi;Massood Tabib-Azar
This study reports the development of an impedimetric sensor for the selective detection of sodium lactate using platinum interdigital electrodes functionalized with a lactate-specific ssDNA aptamer. Designed to overcome the limitations of enzyme-based biosensors, the sensor offers improved stability, reusability, and specificity. The device capacitance increased monotonously as a function of lactate concentration from 100 nM–800 nM with 4.6 pF/nM sensitivity measured at 15 kHz. Atomic force microscope imaging showed lower surface roughness of lactate on aptamer (∼45 nm) compared with glucose (560 nM) and dopamine (890 nM), indicating a higher affinity of the aptamer to bind with sodium lactate that results in a smoother surface.
{"title":"Aptamer-Coated Impedimetric Sensors for Sodium Lactate Detection","authors":"Junaid Ahmed Qureshi;Massood Tabib-Azar","doi":"10.1109/LSENS.2025.3646977","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3646977","url":null,"abstract":"This study reports the development of an impedimetric sensor for the selective detection of sodium lactate using platinum interdigital electrodes functionalized with a lactate-specific ssDNA aptamer. Designed to overcome the limitations of enzyme-based biosensors, the sensor offers improved stability, reusability, and specificity. The device capacitance increased monotonously as a function of lactate concentration from 100 nM–800 nM with 4.6 pF/nM sensitivity measured at 15 kHz. Atomic force microscope imaging showed lower surface roughness of lactate on aptamer (∼45 nm) compared with glucose (560 nM) and dopamine (890 nM), indicating a higher affinity of the aptamer to bind with sodium lactate that results in a smoother surface.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"10 2","pages":"1-3"},"PeriodicalIF":2.2,"publicationDate":"2025-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145982363","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-22DOI: 10.1109/LSENS.2025.3646771
Deepika Sasi;Sundaresan Sabapathy;Thomas Joseph
Distributed acoustic sensing (DAS) enables dense seismic monitoring; however, event detection is challenged by limited labeled data and noise. This letter introduces a semisupervised framework based on multiloss temporal convolutional network, where hybrid masking and multiobjective loss enhance signal-to-noise ratio (SNR) and improve label efficiency. The method achieves 98.75% classification accuracy and 36.55 dB SNR, significantly surpassing semisupervised baselines. To further illustrate adaptability, transfer learning experiment on an external dataset confirms the model’s generalization capability. This label-efficient method advances scalable and robust DAS-based seismic event detection with minimal labeled data requirements.
{"title":"Semi-Supervised Multi-Loss TCN and Transfer Learning for Earthquake Detection in Distributed Fiber-Optic Acoustic Sensing Systems","authors":"Deepika Sasi;Sundaresan Sabapathy;Thomas Joseph","doi":"10.1109/LSENS.2025.3646771","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3646771","url":null,"abstract":"Distributed acoustic sensing (DAS) enables dense seismic monitoring; however, event detection is challenged by limited labeled data and noise. This letter introduces a semisupervised framework based on multiloss temporal convolutional network, where hybrid masking and multiobjective loss enhance signal-to-noise ratio (SNR) and improve label efficiency. The method achieves 98.75% classification accuracy and 36.55 dB SNR, significantly surpassing semisupervised baselines. To further illustrate adaptability, transfer learning experiment on an external dataset confirms the model’s generalization capability. This label-efficient method advances scalable and robust DAS-based seismic event detection with minimal labeled data requirements.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"10 2","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2025-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145982181","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-22DOI: 10.1109/LSENS.2025.3646924
Anuj Kumar Mishra;Aditya Srivastava;Ripul Ghosh
We investigate direction-of-arrival (DoA) estimation for airborne sound sources using a tetrahedral microphone array, addressing key challenges in drone detection and situational awareness applications. Practical deployment of DoA systems is often constrained by computational demands, stringent calibration requirements, and susceptibility to sensor failures. To address these limitations, we propose masked-aware directional attention network (MADANet), a lightweight signal processing pipeline coupled with a pairwise attention-based neural architecture designed for robust performance under sensor failure scenarios. The architecture extracts magnitude, phase, and geometric features for each microphone pair from resampled acoustic signals (4–20 kHz), selects active frames via energy gating, and embeds these features through a shared multilayer perceptron before applying multihead self-attention for adaptive fusion. A structured sensor dropout mechanism is introduced to mask feature pairs from randomly deactivated microphones and normalize attention weights accordingly. Experiments conducted in a semianechoic chamber demonstrate that downsampling to 4 kHz and using four attention heads minimize mean spherical error, approaching the Cramér–Rao lower bound for the given array geometry. The model exhibits strong generalization to single- and double-microphone failure scenarios, maintaining subdegree accuracy.
{"title":"Masked-Aware Directional Attention Network for DOA Estimation Under Sensor Failure Conditions","authors":"Anuj Kumar Mishra;Aditya Srivastava;Ripul Ghosh","doi":"10.1109/LSENS.2025.3646924","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3646924","url":null,"abstract":"We investigate direction-of-arrival (DoA) estimation for airborne sound sources using a tetrahedral microphone array, addressing key challenges in drone detection and situational awareness applications. Practical deployment of DoA systems is often constrained by computational demands, stringent calibration requirements, and susceptibility to sensor failures. To address these limitations, we propose masked-aware directional attention network (MADANet), a lightweight signal processing pipeline coupled with a pairwise attention-based neural architecture designed for robust performance under sensor failure scenarios. The architecture extracts magnitude, phase, and geometric features for each microphone pair from resampled acoustic signals (4–20 kHz), selects active frames via energy gating, and embeds these features through a shared multilayer perceptron before applying multihead self-attention for adaptive fusion. A structured sensor dropout mechanism is introduced to mask feature pairs from randomly deactivated microphones and normalize attention weights accordingly. Experiments conducted in a semianechoic chamber demonstrate that downsampling to 4 kHz and using four attention heads minimize mean spherical error, approaching the Cramér–Rao lower bound for the given array geometry. The model exhibits strong generalization to single- and double-microphone failure scenarios, maintaining subdegree accuracy.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"10 2","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2025-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146082117","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-19DOI: 10.1109/LSENS.2025.3643941
Yogendra Kumar;Mohammad Arif Khan;Avishek Adhikary
Elevators play an essential role for safe and efficient vertical transportation in modern buildings. However, traditional maintenance strategies based on scheduled inspections often fail to detect faults that appear between inspection intervals, this leads to unplanned downtime. This letter presents an Internet of Things-based elevator health monitoring system that integrates vibration sensing, wireless communication, and machine learning to enable real-time condition monitoring. The system utilizes an accelerometer sensor interfaced with an microcontroller to capture elevator vibration and motion data. The collected data are transmitted to a cloud server, where a long short-term memory-based autoencoder is used for anomaly detection by calculating threshold based on reconstruction error. The proposed system detects real-time fault data with 98.33% accuracy and 100% recall, validated through four-month experimental study.
{"title":"An IoT-Based Real-Time Elevator Health Monitoring System Using LSTM Autoencoder","authors":"Yogendra Kumar;Mohammad Arif Khan;Avishek Adhikary","doi":"10.1109/LSENS.2025.3643941","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3643941","url":null,"abstract":"Elevators play an essential role for safe and efficient vertical transportation in modern buildings. However, traditional maintenance strategies based on scheduled inspections often fail to detect faults that appear between inspection intervals, this leads to unplanned downtime. This letter presents an Internet of Things-based elevator health monitoring system that integrates vibration sensing, wireless communication, and machine learning to enable real-time condition monitoring. The system utilizes an accelerometer sensor interfaced with an microcontroller to capture elevator vibration and motion data. The collected data are transmitted to a cloud server, where a long short-term memory-based autoencoder is used for anomaly detection by calculating threshold based on reconstruction error. The proposed system detects real-time fault data with 98.33% accuracy and 100% recall, validated through four-month experimental study.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"10 2","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2025-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145982368","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-19DOI: 10.1109/LSENS.2025.3646168
Lekshmi V;Jose Joseph
Planar inductive temperature sensors, owing to their CMOS compatibility, in-plane geometry, and inherent scalability, are ideal candidates for compact, low-power, and high-performance applications. Geometry optimization serves as a powerful strategy for fully exploiting the potential of inductive temperature sensors. This research draws on geometry optimization to enhance the sensitivity of planar inductive temperature sensors. Among the various geometries evaluated, the hexagonal geometry with optimized dimensions was identified as the most effective configuration. Analytical design, finite element modeling, and experimental characterization were employed to validate this finding. Further optimization revealed that a large inner diameter with a minimal fill ratio yields the highest inductance change, provided that the separation width between turns is not less than the turn width. These findings offer a robust framework for the development of compact, high-sensitivity inductive sensors for temperature monitoring.
{"title":"Sensitivity Enhancement of CMOS-Compatible Planar Inductive Temperature Sensors via Geometric Optimization","authors":"Lekshmi V;Jose Joseph","doi":"10.1109/LSENS.2025.3646168","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3646168","url":null,"abstract":"Planar inductive temperature sensors, owing to their CMOS compatibility, in-plane geometry, and inherent scalability, are ideal candidates for compact, low-power, and high-performance applications. Geometry optimization serves as a powerful strategy for fully exploiting the potential of inductive temperature sensors. This research draws on geometry optimization to enhance the sensitivity of planar inductive temperature sensors. Among the various geometries evaluated, the hexagonal geometry with optimized dimensions was identified as the most effective configuration. Analytical design, finite element modeling, and experimental characterization were employed to validate this finding. Further optimization revealed that a large inner diameter with a minimal fill ratio yields the highest inductance change, provided that the separation width between turns is not less than the turn width. These findings offer a robust framework for the development of compact, high-sensitivity inductive sensors for temperature monitoring.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"10 2","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2025-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145929410","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The growing threat of heavy metal ion (HMI) contamination in drinking water calls for highly sensitive, rapid, and field-deployable detection technologies. Here, we report a p-type reduced graphene oxide (rGO)/graded ZnO field-effect transistor (FET) that enables tunable, low-level detection of copper [Cu(II)] and iron [Fe(II)] ions in aqueous media. The vertically aligned ZnO nanorods, synthesized via a seed-layer-assisted CBD method, serve as both the gate dielectric and receptor layer, while rGO functions as the conducting channel. The heterostructured FET demonstrates strong gate-voltage-dependent selectivity, showing a maximum response of $sim$4406% for Cu(II) ions at $V_{GS}$ = –4 V and $sim$4699% for Fe(II) at $V_{GS}$ = –5 V. Low limits of detection of $sim$1.0 ppb for both Cu(II) and Fe(II) ions are achieved experimentally, with rapid response and recovery times of 4 and 3.5 s for Cu(II) ion and 10 and 3 s for Fe(II) ion, respectively. The sensing mechanism is attributed to adsorption-driven reduction of target ions on the ZnO surface, which modulates the interfacial dipole and channel conductivity of the rGO layer. This work establishes a cost-effective, real-time monitoring of multiple HMIs and highlights the potential of graded ZnO–rGO FETs for next-generation environmental water quality assessment.
{"title":"rGO/graded ZnO-Based FET for Tunable Heavy Metal Ion Detection in Water","authors":"Arijit Pattra;Sampurna Mukherjee;Bidesh Mahata;Tanmoy Jana;Sayan Dey","doi":"10.1109/LSENS.2025.3646351","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3646351","url":null,"abstract":"The growing threat of heavy metal ion (HMI) contamination in drinking water calls for highly sensitive, rapid, and field-deployable detection technologies. Here, we report a p-type reduced graphene oxide (rGO)/graded ZnO field-effect transistor (FET) that enables tunable, low-level detection of copper [Cu(II)] and iron [Fe(II)] ions in aqueous media. The vertically aligned ZnO nanorods, synthesized via a seed-layer-assisted CBD method, serve as both the gate dielectric and receptor layer, while rGO functions as the conducting channel. The heterostructured FET demonstrates strong gate-voltage-dependent selectivity, showing a maximum response of <inline-formula><tex-math>$sim$</tex-math></inline-formula>4406% for Cu(II) ions at <inline-formula><tex-math>$V_{GS}$</tex-math></inline-formula> = –4 V and <inline-formula><tex-math>$sim$</tex-math></inline-formula>4699% for Fe(II) at <inline-formula><tex-math>$V_{GS}$</tex-math></inline-formula> = –5 V. Low limits of detection of <inline-formula><tex-math>$sim$</tex-math></inline-formula>1.0 ppb for both Cu(II) and Fe(II) ions are achieved experimentally, with rapid response and recovery times of 4 and 3.5 s for Cu(II) ion and 10 and 3 s for Fe(II) ion, respectively. The sensing mechanism is attributed to adsorption-driven reduction of target ions on the ZnO surface, which modulates the interfacial dipole and channel conductivity of the rGO layer. This work establishes a cost-effective, real-time monitoring of multiple HMIs and highlights the potential of graded ZnO–rGO FETs for next-generation environmental water quality assessment.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"10 2","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2025-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145929515","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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