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"IEEE Sensors Alert" is a pilot project of the IEEE Sensors Council. Started as one of its new initiatives, this weekly digest publishes teasers and condensed versions of our journal papers in layperson's language.
Articles Posted in the Month (April 2026)
Impact Solenoid Modeling for Current-Dependent Piston Position Estimation
Author: Spekreijse Sam, Hayes Michael, Yang Le
Published in: IEEE Sensors Journal (Volume: 25, Issue: 24, December 2025)
Summary Contributed by: Spekreijse (Author)
Impact solenoids are specialized electromagnets with captive pistons that convert electrical energy into mechanical impact. They are used in industrial automation, automotive systems, and electromechanical devices, where precise piston position measurements are crucial. However, it requires dedicated sensors, which increases costs. This study presents a method for tracking the piston position of off-the-shelf solenoids during impacts by measuring the coil current. This sensorless approach achieves near-accurate position estimation across various solenoid models.
Highly Enhanced Sensing Performances of Si-Based Electrolyte-Gated Transistor (EGT) Using Silver Nanowire Coating Method in Chikungunya Detection
Author: Lee Jeong-soo, Do Jeonghyeon, Kim Kihyun, Shin Seong-Hwan, Son Jongmin
Published in: IEEE Sensors Journal (Volume: 25, Issue: 24, December 2025)
Summary Contributed by: Jeong-Soo Lee (Author)
Chikungunya virus (CHIKV) remains challenging to diagnose early due to symptom overlap with other mosquito-borne diseases. This work presents a highly sensitive silicon-based biosensor enhanced with a silver nanowire-coated gate. These nanowires boost the electrical performance and surface reactivity of the sensor, enabling rapid, accurate, and ultralow-concentration detection of viral proteins. The approach significantly improves sensitivity and selectivity, offering a promising route toward early detection and reliable point-of-care diagnostics.
Development of a New Around-the-Ear Electroencephalography Device for Passive Brain–Computer Interface Applications
Author: Ahn Hyunjin, Im Chang-hwan, Kim Jung-Hwan, Kim Minsu, Kim Seonho, Kim Suhye, Kim Yoosung, Oh Eunkyu, Park Dasom
Published in: IEEE Sensors Journal (Volume: 25, Issue: 18, September 2025)
Summary Contributed by: Hyunjin Ahn (Author)
A brain-computer interface (BCI) enables users to control external devices using their brain signals. This paper introduces a compact around-the-ear electroencephalography device that captures brain signals through discreet sensors placed around the ear. It demonstrates reliable signal quality, better performance across passive BCI tasks, preference prediction, and drowsiness detection. This wearable device paves the way for monitoring brain activity in fields such as neuromarketing and neuroeducation, demonstrating its practical potential.
Easy-to-Fabricate Digital Microfluidic Chip Based on PCB With Glucose Enzyme-Free Detection Function
Author: Xu Chuanpei, Cen Yuanyin, Han Guo-Cheng, Huang Heyue, Li Siyu, Zhan Tao, Zhang Bo
Published in: IEEE Sensors Journal (Volume: 25, Issue: 24, December 2025)
Summary Contributed by: Heyue Huang and Chuanpei Xu (Authors)
Glucose is a crucial health indicator. This study presents an easy-to-fabricate digital microfluidic chip built on a printed circuit board (PCB), enabling precise control of sample and sodium hydroxide (NaOH) droplets to create an alkaline environment necessary for enzyme-free glucose detection. The Simple fabrication process, reliable sensing performance, and low cost of this portable PCB-based microfluidic device open possibilities for lab-on-chip applications, point-of-care diagnostics, and wearable devices for broader use.
Published in: IEEE Sensors Journal (Volume: 25, Issue: 24, December 2025)
Summary Contributed by: Wu Zhicheng (Author)
Moisture sensors have a wide range of applications in environmental monitoring, agriculture, food processing, healthcare, and scientific research. This study fabricates porous polyimide (PI) moisture sensors using the nonsolvent-induced phase separation (NIPS) method. By balancing the micropore size and imidization degree of porous PI films, it achieves fast response (9 s) and low humidity hysteresis (2.8% RH) in moisture sensing, providing a novel strategy for developing next-generation high-performance moisture sensors.
Temperature and Strain Sensing Characteristics of a 128° YX-Cut LiNbO3 Rayleigh-Mode SAW Sensor From Room to Cryogenic Temperatures
Author: Li Fang, E Fernando Camino, Kohler Michael, Luo Jiaxing, Voiculescu Ioana R.
Published in: IEEE Sensors Journal (Volume: 25, Issue: 24, December 2025)
Summary Contributed by: Fang Li (Author)
Surface acoustic wave (SAW) sensors offer a unique way to monitor extreme cryogenic environments without batteries or wiring. This study explores a lithium-niobate SAW device that accurately detects temperature and mechanical strain from room temperature down to cryogenic levels. It also analyzes how acoustic waves behave under various conditions, emphasizing the sensor's reliability and sensitivity, and advancing its applications in aerospace, cryogenic engineering, biomedicine, and next-generation wireless sensing technologies.
A Molecular Imprinted Quartz Crystal Microbalance Sensor for Reliable Detection of Alpha-Terpineol in Various Pine Essential Oils
Author: Banerjee Roy Runu, Bhattacharyya Banerjee Mahuya, Gangopadhyay Deepam, Kundu Sumit, NAG SHREYA, Pramanik Panchanan
Published in: IEEE Sensors Journal (Volume: 25, Issue: 15, August 2025)
Summary Contributed by: Runu Banerjee Roy (Author)
Alpha-terpineol (A-Te), a naturally occurring compound found in essential oils, has antimicrobial, antioxidant, and anti-inflammatory properties. This paper introduces a novel molecularly imprinted Quartz Crystal Microbalance (QCM) sensor for detecting A-Te in pine essential oils. This portable, robust molecularly imprinted polymer (MIP)-based QCM sensor combines high sensitivity, selectivity, and machine learning technology for qualitative determination of A-Te. The targeted detection method enables rapid authentication and quality assurance of aromatic oils.
Detection of CWA Simulants by Electronic Nose Based on Low-Powered MEMS Gas Sensor Array
Author: Cheng Zhenxing, Sun Xuhui, Hu Xiaochun, Shen Qingqing, Tao Yi, Xu Mengxue, Zhang Hongpeng, Zhang Pingping, Zhang Shumin, Zhong Yihong
Published in: IEEE Sensors Journal (Volume: 25, Issue: 15, August 2025)
Summary Contributed by: Saurabh Dubey
Early detection of toxic chemical warfare agents (CWAs) requires reliable portable systems capable of real-time sensing. This study presents a novel MEMS-based electronic nose that integrates a 24-sensor metal-oxide array to identify CWA simulants via unique response fingerprints. Its low-power microhot-plate sensors deliver a quick response time, and machine learning achieves 99% accuracy across different environments, ensuring robust, sensitive, and highly selective CWA monitoring for defense and public safety applications.
Remote Vital Sign Monitoring With IMU-Assisted Handheld IR-UWB Radar Sensor
Author: Cho Sung Ho, Abdullah Sohaib, Ahmed Shahzad, Yoon Seongkwon
Published in: IEEE Sensors Journal (Volume: 25, Issue: 16, August 2025)
Summary Contributed by: Sung Ho Cho (Author)
Radar-based human vital sign measurement has attracted significant attention owing to its noncontact, non-invasive, privacy-preserving nature. However, the potential of handheld radar devices remains to be explored. The novel approach presented here extracts heart rate (HR) and breathing rate (BR) using a handheld impulse-radio ultrawideband (IR-UWB) radar equipped with an inertial measurement unit (IMU) sensor. It will open the door to continuous health and wellness tracking and outdoor emergency assistance.
Certifying fruit ripeness at harvest is crucial for ensuring optimal quality, effective storage, and successful market distribution. This study presents a novel low-cost multispectral device that integrates a broadband light-emitting diode (LED) and visible and near-infrared (VIS–NIR) sensors to assess fruit ripeness in the field. It categorizes multiple ripeness classes using automatic feature selection and machine-learning algorithms, achieving high accuracy and providing an affordable, robust solution for smart harvesting.
The recent COVID outbreaks highlighted the need for breathing rate monitoring and increased the demand for hospitalized patients. Monitoring breathing rate is vital for diagnosing diseases and observing patients with pulmonary conditions. The pros and cons of different techniques are studied and categorized under contact and remote modes of respiratory monitoring systems. Various Radar-based methods found to be more suitable for respiration monitoring are discussed.
Radar detection of smaller targets requires lowering the radar cross-section and velocity thresholds. With it, an abundance of target signatures gets generated, making it necessary to classify only relevant targets. Micro-motions of targets are significant characteristics. Micro-Doppler signatures have emerged as an effective method of classifying such targets. The study presents a systematic review of various micro-Doppler-based radar target signature analysis and classification techniques.
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