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Laser-assisted manufacturing for sensors

INTERNATIONAL JOURNAL OF EXTREME MANUFACTURING 2025年第4期7卷 042008-042008页

作者： Han, Jing Xin, Di Pang, Jinbo Zhao, Lili Sun, Dehui Zheng, Yang Liu, Xiaoyan Zhao, Zhenhuan Zhang, Xiaoli Sun, Qijun Liu, Hong Zhou, Weijia Univ Jinan Inst Adv Interdisciplinary Res IAIR Sch Chem & Chem Engn Jinan 250022 Peoples R China Chinese Acad Sci Beijing Inst Nanoenergy & Nanosyst Beijing 101400 Peoples R China Beijing Informat Sci & Technol Univ Key Lab Minist Educ Optoelect Measurement Technol Beijing 100192 Peoples R China Tufts Univ Dept Chem Medford MA 01255 USA RMIT Univ Australian Res Council Ctr Excellence Green Electr Melbourne Vic 3001 Australia Univ Chinese Acad Sci Sch Nanosci & Technol Beijing 100049 Peoples R China Guangxi Univ Ctr Nanoenergy Res Sch Phys Sci & Technol Nanning 530004 Peoples R China Shandong Univ State Key Lab Crystal Mat Jinan 250100 Peoples R China

The laser-assisted manufacturing technology has significant advantages in meeting various demands such as complex structures, functional integration, customized devices, and cost-effectiveness, which makes it a highly attractive option for fabricating sensors. In this review, the latest advancements and strategies in intelligent sensor development through laser processing were surveyed and outlined following the interaction of laser and materials. Laser-assisted manufacturing technologies have been extensively applied in materials science and device processing. Firstly, laser technology can be utilized in a wide range of materials, encompassing carbon-based materials, metals, and metallic oxides. In the field of device scale processing, laser manufacturing is widely used in micro/nano structures, planar device construction, and stereoscopic electronic devices such as cutting, engraving, and lithography. Additionally, laser technology provides robust support for sensor applications, covering fields such as pressure sensing, temperature sensing, gas sensing, and biosensors. Furthermore, laser considerably serves in real application areas such as multifunctional sensing systems, actuators, and robots. The widespread application of laser manufacturing technology in sensor platform fabrication offers effective solutions for realizing the miniaturization, multifunctionality, and integration of sensors. A comprehensive review of laser-assisted manufacturing techniques for sensor *** of novel laser-based processes for enhancing sensor performance and *** of crucial advantages and challenges in using lasers for precision sensor manufacturing.

关键词： laser manufacturing sensors material synthesis device structures functionality

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Resistive pressure sensors through advanced pad printing techniques for integration in robotic gripper systems

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ENGINEERING RESEARCH EXPRESS 2025年第1期7卷 015288-015288页

作者： Bavil, Ahad Khabbaz Nebipasagil, Esma Nur Tekcin, Meltem Kursun, Senem Istanbul Tech Univ Dept Mechatron Engn TR-34469 Istanbul Turkiye Istanbul Tech Univ Dept Text Engn TR-34437 Istanbul Turkiye Istanbul Tech Univ Dept Mech Engn TR-34437 Istanbul Turkiye CETEX Ctr Excellence Text ITU ARI TEKNOKENT Istanbul Turkiye

In recent years, the application of fabric sensors has significantly increased due to their unique properties. This article presents a novel approach to designing and fabricating a textile-based pressure sensor specifically for robotic grippers, utilizing the pad printing technique. By incorporating inks infused with conductive nanoparticles, we enhance the conductivity of the printed designs on various fabrics. Key factors influencing the design and fabrication of this pressure sensor include the type of fabric, ink composition, and the number of print passes. The purpose of this study was to determine and stabilize the ideal fabrication parameters of the proposed sensor based on the targeting performance in the robotic gripper by experimentally examining the effective parameters. The performance of the fabricated sensors is assessed based on critical metrics such as sensitivity, linearity, repeatability, and fatigue resistance. The results indicate that sensors printed on sateen woven fabric, using five print passes and an ink ratio of 25% silver to 75% carbon, exhibited superior performance compared to other configurations. This research highlights the potential of textile-based sensors in enhancing the functionality of robotic grippers.

关键词： textile-based resistive pressure sensor pad printing conductive ink with nano particles robotic gripper flexible sensor e-textiles

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Covid-19 Detection Using Organic LED and Photodiode-Based sensor Device

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IEEE sensors JOURNAL 2024年第24期24卷 40678-40684页

作者： Yadav, Sugandha Mittal, Poornima Negi, Shubham Delhi Technol Univ Dept Elect & Commun Engn Delhi India Chandigarh Univ Mohali Elect & Commun Dept Mohali 140413 Punjab India

The utilization of fluorescence detection for various biomedical applications such as oximeter sensors, cancer detection, monitoring of protein-DNA interactions, heart stroke detection, etc. has been on the rise which was first introduced by Heyduk and Lee in 1990. The integration of organic light emitting diode and organic photodiode (OPD) is well suited for these kinds of health-care detections. In this article, a methodology is proposed for Covid-19 detection which consists of a highly flexible blue organic LED (OLED), an OPD, and a human saliva sample. An in-depth investigation is being performed on both the OLED and OPD devices to make these devices suitable for the detection of the SARS-COV-2 virus inside human saliva samples. The presented methodology is based on fluorescence detection. Herein, the OLED emits an excitation wavelength of 470 nm and OPD produces two different currents 63.5 and 37.2 mA corresponding to the emission wavelengths of 490 and 525 nm, correspondingly. It is concluded here that if the OPD produces 63.5 mA current, the person is infected by Covid-19 and if it produces 37.2 mA current, the person is healthy. This article also compares the proposed methodology with some other researcher's work. The proposed OLED-OPD integration may be utilized for other sensing applications such as environmental monitoring, multispectral sensing, optical sensing, IoT devices, etc.

关键词： Covid-19 organic LED (OLED) organic photodiode (OPD) sensor V-OTFT

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Autoencoder reconstruction residual-Wasserstein distance based in-situ calibration for indoor environment spatial expansion virtual sensors☆

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ENERGY AND BUILDINGS 2025年 333卷

作者： Shin, Hakjong Jo, Seng-Kyoun Choi, Won-Kyu Elect & Telecommun Res Inst ETRI Agr Anim Aquaculture & Ocean Intelligence Res Ctr Daejeon South Korea

Increasing reliance on digital twin technology for managing indoor environments necessitates the development of spatial expansion virtual sensors (SEVS). However, in practical applications, SEVS performance often deteriorates due to shifts in data distribution and environmental conditions, presenting challenges for consistent reliability. Most existing SEVS research has primarily focused on initial model development, with limited consideration to in-situ calibration strategies. This study introduces an autoencoder reconstruction residualWasserstein distance (AR-WD)-based error estimation model, designed for spatial expansion virtual sensors with the primary objective of enhancing their performance in practical applications. The proposed model utilizes residuals from autoencoders and Wasserstein features, which can be derived without additional sensor installations, for real-time calibration. A comprehensive evaluation was conducted using temperature data from a pigsty, where the AR-WD model demonstrated robust performance across various machine learning algorithms, particularly with random forest and XGBoost, showing high predictive accuracy with a mean absolute error as low as 0.086. These findings suggest that the integration of AR-WD features significantly enhances the reliability and accuracy of virtual sensors. In addition, the AR-WD model leverages the unique characteristics of SEVS to enable real-time error estimation based solely on input data variations, thereby addressing common limitations of non-intrusive calibration methods. This research not only advances the field of virtual sensor development but also provides critical insights for optimizing sensor systems in complex indoor settings.

关键词： Digital twin Virtual sensor Indoor environment Spatial expansion Calibration

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Labyrinthine Wrinkle-Patterned Fiber sensors Based on a 3D Stress Complementary Strategy for Machine Learning-Enabled Medical Monitoring and Action Recognition

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SMALL 2025年第5期21卷 e2407390页

作者： Lv, Yongming Chu, Zhenming Huang, Desheng Fan, Xiaoxing Zhang, Wei Liaoning Univ Sch Phys Liaoning Key Lab Optoelect Funct Devices & Detect Liaoning 110036 Peoples R China China Med Univ Sch Publ Hlth Shenyang 110122 Liaoning Peoples R China China Med Univ Sch Intelligent Med Shenyang 110122 Liaoning Peoples R China

Fiber strain sensors show good application potential in the field of wearable smart fabrics and equipment because of their characteristics of easy deformation and weaving. However, the integration of fiber strain sensors with sensitive response, good stretchability, and effective practical application remains a challenge. Herein, this paper proposes a new strategy based on 3D stress complementation through pre-stretching and swelling processes, and the polydimethylsiloxane (PDMS)/silver nanoparticle (AgNPs)/MXene/carbon nanotubes (CNTs) fiber sensor with the bilayer labyrinthian wrinkles conductive network on the PU fiber surface is fabricated. Benefiting from the wrinkled structure and the synergies of sensitive composite materials, the fiber sensor exhibits good stretchability (>150%), high sensitivity (maximum gauge factor is 57896), ultra-low detection limit (0.1%), fast response/recovery time (177/188 ms) and good long-term durability. It can be used as Morse code issuance and recognition to express the patient's symptoms and feelings. Further, the sensor enables comprehensive human movement monitoring and collects data of different characteristics with the assistance of machine learning, different letters/numbers are recognized and predicted with an accuracy of 99.17% and 99.33%. Therefore, this fiber sensor shows potential as a new generation of flexible strain sensors with applications in medical monitoring and human-computer interaction.

关键词： 3D stresses fiber strain sensors labyrinthine wrinkles machine learning predictions motion monitoring

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Fault location and type identification method for current and voltage sensors in traction rectifiers

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JOURNAL OF POWER ELECTRONICS 2025年第4期25卷 666-674页

作者： Yu, Yunjun Song, Yunquan Tao, Hongwei Nanchang Univ Sch Informat Engn Nanchang Peoples R China Nanchang Univ Minist Educ Engn Res Ctr Watershed Carbon Neutralizat Nanchang Peoples R China

The reliability of rectifiers is regarded as one of the most important factors in traction systems. Unexpected faults occurring in sensors can degrade the performance and lead to secondary faults. Accordingly, a sensor fault diagnosis method is proposed in this paper. It can locate faults and identify fault types. Three high-incidence fault types in current and voltage sensors have been taken into consideration. Only the current residual is needed in the process of fault diagnosis. No additional sensors are required in this method. First, a traction rectifier model is developed. Then, a grid current estimator is constructed, the residual is acquired and applied to fault detection. Next, the residual is analyzed under different kinds of sensor faults. Fault diagnosis functions are constructed and the faults can be diagnosed. Finally, an experiment test is processed to demonstrate the effectiveness of the proposed method.

关键词： sensor fault diagnosis Fault location Fault type identification Rectifiers

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An Overview of Software sensor Applications in Biosystem Monitoring and Control

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sensors 2024年第20期24卷 6738页

作者： Badreldin, Nasem Cheng, Xiaodong Youssef, Ali Univ Manitoba Dept Soil Sci 13 Freedman Crescent Winnipeg MB R3T 2N2 Canada Wageningen Univ & Res Dept Plant Sci Math & Stat Methods Grp Biometris NL-6700 AA Wageningen Netherlands Wageningen Univ & Res Adaptat Physiol Grp POB 338 NL-6700 AH Wageningen Netherlands

This review highlights the critical role of software sensors in advancing biosystem monitoring and control by addressing the unique challenges biological systems pose. Biosystems-from cellular interactions to ecological dynamics-are characterized by intrinsic nonlinearity, temporal variability, and uncertainty, posing significant challenges for traditional monitoring approaches. A critical challenge highlighted is that what is typically measurable may not align with what needs to be monitored. Software sensors offer a transformative approach by integrating hardware sensor data with advanced computational models, enabling the indirect estimation of hard-to-measure variables, such as stress indicators, health metrics in animals and humans, and key soil properties. This article outlines advancements in sensor technologies and their integration into model-based monitoring and control systems, leveraging the capabilities of Internet of Things (IoT) devices, wearables, remote sensing, and smart sensors. It provides an overview of common methodologies for designing software sensors, focusing on the modelling process. The discussion contrasts hypothetico-deductive (mechanistic) models with inductive (data-driven) models, illustrating the trade-offs between model accuracy and interpretability. Specific case studies are presented, showcasing software sensor applications such as the use of a Kalman filter in greenhouse control, the remote detection of soil organic matter, and sound recognition algorithms for the early detection of respiratory infections in animals. Key challenges in designing software sensors, including the complexity of biological systems, inherent temporal and individual variabilities, and the trade-offs between model simplicity and predictive performance, are also discussed. This review emphasizes the potential of software sensors to enhance decision-making and promote sustainability in agriculture, healthcare, and environmental monitoring.

关键词： software sensors biosystems monitoring control machine-learning digital agriculture

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Leader-Follower Control of Rehabilitative Soft Glove Based on Collaborative Sensing and Fine Motion Recognition

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IEEE sensors JOURNAL 2024年第19期24卷 30329-30339页

作者： Zhang, Jianhua Li, Xiangli Li, Hui Wang, Hui Zhang, Jianjun Hao, Yufei Univ Sci & Technol Beijing Sch Mech Engn Beijing 100083 Peoples R China Hebei Univ Technol Sch Mech Engn Tianjin 300401 Peoples R China

Rehabilitative gloves with leader-follower control offer a promising approach for stroke patients with impaired hand function. However, the current integration of sensors in rehabilitative soft gloves is limited, which poses challenges in achieving position feedback and desired bending angles of actuators. Moreover, fine movement perception and recognition add complexity to leader-follower control. Here, we propose a rehabilitative soft glove system with cooperative sensing and fine gesture recognition, utilizing a carbon black (CB) strain sensor and electromyography sensor. The strain sensor has high flexibility and stretchability, and we conducted a comprehensive analysis of its electrical characteristics. It demonstrates an impressive 100% stretchability with a gauge factor of 1.14-1.34. By seamlessly integrating the strain sensors into a data glove, we successfully monitored finger movement and grip. We propose two leader-follower control methods, namely strain sensor-based position control and cooperative sensing-based fine dynamic gesture recognition control. In the former approach, we calibrated of all sensors integrated in the data glove and actuators, enabling position control of the rehabilitative soft glove through an incremental PID controller. For fine dynamic gesture recognition, we introduce a ResNet-ConvLSTM-self-attention (RCSA) deep learning framework that eliminates the need for sensor calibration and achieves an impressive recognition accuracy of 96.20%. Finally, we validate the effectiveness of our proposed control scheme through online experiments with the rehabilitative soft glove.

关键词： Collaborative sensing leader-follower control rehabilitative soft glove strain sensor Collaborative sensing leader-follower control rehabilitative soft glove strain sensor

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A fiber-shaped sensor constructed by coaxial wet-spinning for dual-mode sensing

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JOURNAL OF MATERIALS CHEMISTRY A 2025年第8期13卷 5870-5881页

作者： Ma, Duixin Fang, Huayang Sun, Jianping Jiang, Tao Guangxi Univ State Key Lab Featured Met Mat & Life Cycle Safety Nanning 530004 Peoples R China Guangxi Univ Finance & Econ Sch Journalism & Cultural Commun Nanning 530004 Guangxi Peoples R China

With the booming development of smart electronic devices, the forms of wearable sensors are gradually diversifying. However, integrating dual sensing capabilities into a single sensor for decoupled strain and humidity detection remains a significant challenge. In this study, we report a flexible dual-modal sensor designed with a "skin-core" structure that integrates pressure and humidity sensing layers, enabling decoupled monitoring of pressure and humidity. Using a coaxial wet-spinning method, we fabricated multifunctional sensing fibers with MXene/CNF as the core and cationic cellulose as the skin. By controlling the ratio of the spinning solution for the core layer, the resulting MXene/CNF@cationic cellulose aerogel fiber (MCC) pressure sensor exhibits high sensitivity (120 kPa-1), rapid response time (50 ms for response, 55 ms for recovery), and excellent cycling stability under compression. Furthermore, the unique structure of the coaxially spun aerogel and the inherent properties of the MCC fiber material endow the sensor with outstanding cycling stability, fast moisture absorption and desorption responses (response time of 9.43 s and recovery time of 5.3 s), and excellent moisture absorption and desorption characteristics. This study promotes the effective utilization of cellulose-based materials in wearable sensing and health management, expands how sensors can be worn, and lays the foundation for the integration of sensors with garments.

关键词： Aerogels

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Application of FBG sensor in health monitoring of engineering building structure: a review

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sensor REVIEW 2025年第1期45卷 129-145页

作者： Zhou, Chenxia Jia, Zhikun Song, Shaobo Luo, Shigang Zhang, Xiaole Zhang, Xingfang Pei, Xiaoyuan Xu, Zhiwei Tiangong Univ Sch Text Sci & Engn Tianjin Peoples R China HeBeihuabei Petr Engn Construct Co Ltd Shijiazhuang Hebei Peoples R China Carbon Technol Grp Co Ltd Tianjin Peoples R China

Purpose - The aging and deterioration of engineering building structures present significant risks to both life and property. Fiber Bragg grating (FBG) sensors, acclaimed for their outstanding reusability, compact form factor, lightweight construction, heightened sensitivity, immunity to electromagnetic interference and exceptional precision, are increasingly being adopted for structural health monitoring in engineering buildings. This research paper aims to evaluate the current challenges faced by FBG sensors in the engineering building industry. It also anticipates future advancements and trends in their development within this field. Design/methodology/approach - This study centers on five pivotal sectors within the field of structural engineering: bridges, tunnels, pipelines, highways and housing construction. The research delves into the challenges encountered and synthesizes the prospective advancements in each of these areas. Findings - The exceptional performance of FBG sensors provides an ideal solution for comprehensive monitoring of potential structural damages, deformations and settlements in engineering buildings. However, FBG sensors are challenged by issues such as limited monitoring accuracy, underdeveloped packaging techniques, intricate and time-intensive embedding processes, low survival rates and an indeterminate lifespan. Originality/value - This introduces an entirely novel perspective. Addressing the current limitations of FBG sensors, this paper envisions their future evolution. FBG sensors are anticipated to advance into sophisticated multi-layer fiber optic sensing networks, each layer encompassing numerous channels. Data integration technologies will consolidate the acquired information, while big data analytics will identify intricate correlations within the datasets. Concurrently, the combination of finite element modeling and neural networks will enable a comprehensive simulation of the adaptability and longevity of FBG sensors in their

关键词： FBG sensor Engineering building structure Health monitoring Future development trend

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