IEEE SENSORS JOURNAL
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2024
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IF 4.3
Meng Li,Zanhong Deng,Ruofan Zhang,Junqing Chang,Fedor S. Fedorov,Gang Meng
Monitoring toxic volatile sulfur compounds (VSCs) by metal oxide semiconductor (MOS) sensors has gainedmuch attention for various applications including smart factory and health screening. Though room temperature (RT) operation is highly preferred due to tiny power consumption and minimal explosion risk of VSCs, poor selectivity and insufficient molecule features acquired at RT pose a big challenge. Herein, a 16-channel MOS based electronic nose (e-nose) has been integrated in a glass wafer with ITO interdigital electrode arrays, visible light modulation and three kinds of feature extraction methods have been proposed to extract the (subtle) features of those VSCs molecules. Combining the transient e-nose response characteristics generated by visible light modulation and convolutional neural network (CNN) algorithm, a high prediction accuracy of 99.2% towards five kinds of VSCs with varying concentrations could be achieved. Furthermore, SHapley Additive exPlanations (SHAP) approach has been employed to estimate the contribution of individual sensors in prediction models for the optimization of model computational complexity and sensor array size. This work sheds light on the rational screening of useful sensors for constructing high-performance e-nose with minimal costs for various applications.
研究领域:
传感器技术
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健康监测
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人工智能应用
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DOI
高影响因子
ADVANCED MATERIALS
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2025
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IF 26.8
Jun Cheng,Yao Shen,Yulu Gu,Tongyue Xiang,Hui Shen,Yi Wang,Zhenyang Hu,Zhen Zheng,Zhilong Yu,Qin Wu,Yinghui Wang,Tiancong Zhao,Yunfei Xie
A low-cost, high-precision, and secure real-time system for monitoring food freshness can significantly improve spoilage issues, yet traditional colorimetric sensor arrays often suffer from chemical dyes’ high toxicity and limited color changes. Here, a metal-polyphenol network colorimetric sensor array (MPN-CSA) is built for detecting total volatile base nitrogen (TVB-N) markers of meat freshness. The multi-level competitive coordination process between the metal-polyphenol system and amine substances endows the system with color changes far beyond those of traditional dyes (reaching a detection limit of 300 ppb). By integrating convolutional neural network (CNN) technology, an online platform is developed for monitoring meat freshness, achieving an overall detection accuracy rate of 99.83%. This environmentally friendly, economically viable MPN-CSA that monitors the freshness of meat in complex storage environments can be incorporated into food packaging boxes, enabling consumers and suppliers to assess the freshness of meat in real-time, thus helping to reduce food waste and prevent foodborne illnesses.
研究领域:
食品科学
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化学传感
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人工智能应用
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DOI
PubMed
高影响因子
JOURNAL OF FOOD ENGINEERING
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2023
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IF 5.5
Ya Liu,Yueying Zhang,Feiwu Long,Jinrong Bai,Yina Huang,Hong Gao
The accumulation of lipid oxidation and biogenic amines is the primary cause of pork sausage spoilage during storage. Therefore, this study aimed to develop a colorimetric microfluidic paper analytical device (μPAD) that specifically responded to these two factors to evaluate sausage spoilage during storage. A dataset consisting of 4096 images of μPADs was collected, and a convolutional neural networks (CNN)-based classification model (Resnet50) was trained and tested with an accuracy, F1 score, and recall rate of 97.10%, 97.14%, and 99.17%, respectively. Additionally, the CNN-based model was integrated into a smartphone application with user-friendly interfaces catering to both professional and non-professional users. Furthermore, the classification method was validated by predicting the shelf life of sausages stored at different temperatures. Compared to conventional methods, this approach provided a cost-effective, rapid, and portable detection method for assessing pork sausage freshness while broadening the application of smartphone-based colorimetric μPADs in food safety and quality control.
研究领域:
食品科学
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人工智能
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分析化学
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DOI
高影响因子
Soft Robotics
·
2023
·Vol. 10
·期 3
·443-453
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IF 7.9
Tiantong Wang,Yunbiao Zhao,Qining Wang
Hand gesture recognition, one of the most popular research topics in human–machine interaction, is extensively used in visual and augmented reality, sign language translation, prosthesis control, and so on. To improve the flexibility and interactivity of wearable gesture sensing interfaces, flexible electronic systems for gesture recognition have been widely studied. However, these systems are limited in terms of wearability, stability, scalability, and robustness. Herein, we report a flexible wearable hand gesture recognition system that is based on an iontronic capacitive pressure sensing array and deep convolutional neural networks. The entire capacitive array is integrated into a flexible silicone wristband and can be comfortably and conveniently wrapped around the wrist. The pressure sensing array, which is composed of an iontronic film sandwiched between two flexible screen-printed electrode arrays, exhibits a high sensitivity (775.8 kPa−1), fast response time (65 ms), and high durability (over 6000 cycles). Image processing techniques and deep convolutional neural networks are applied for sensor signal feature extraction and hand gesture recognition. Several contexts such as intertrial test (average accuracy of 99.9%), intersession rewearing (average accuracy of 93.2%), electrode shift (average accuracy of 83.2%), and different arm positions during measurement (average accuracy of 93.1%) are evaluated.
研究领域:
生物医学工程
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人工智能
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传感器技术
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产品关联
DOI
PubMed
高影响因子
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
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2025
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IF 5.9
Hao-Yuan Cao,Yingying Hu,Liang Fang,Bin Liu,Juan Liu,Hong Yang,Yue Zhang,Yue Fu,Wenbo Xiao,Qiang Wu
A 1-D convolutional neural network (1-D CNN) is proposed to extract information collected from a U-shape single-mode-tapered seven core-single (STSS)-mode structure interferometer and demonstrated by measuring surrounding refractive index (RI) as a typical example of its application. Compared with the traditional dip/peak wavelength tracking method, the 1-D CNN method can effectively improve the accuracy of measurement through extracting full-spectrum information. The coefficient of determination ( $R^{2}$ ) of the RI predicted by 1-D CNN is as high as 0.992. At the same time, the effects of bandwidth and sampling points on the RI measurement accuracy are studied; by reducing the spectral sampling resolution and bandwidth, the prediction results are higher than 0.990 and 0.989, and the results show that the method of machine learning can adapt to the low sampling frequency and bandwidth. The proposed RI optical fiber sensing system has good application potential in medical and environmental monitoring fields.
研究领域:
生物医学工程
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光学传感
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分子诊断
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环境监测
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医疗检测技术
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产品关联
DOI
高影响因子
IEEE SENSORS JOURNAL
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2025
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IF 4.5
Wentao Dong,Bo Huang,Kaiqi Sheng,Xiaopeng Qiu,Xiao Chen
Any loosening of bolts may lead to a potential threat to the running stability of trains. Therefore, new sensors should be developed to the loosening status monitoring of bolts under the limited space and complicated environment for enhancing the operation safety of trains. Flexible self-powered vibration triboelectric nanogenerator (V-TENG) sensor based on conductive hydrogel is designed to the axle box bolt tightness monitoring without external power supply. The flexible materials are selected to V-TENG sensor for the adaptability to the curved surface of the axle box. By the optimal resistance, the V-TENG sensor, equipped with conductive hydrogel electrodes, achieves a maximum power output of 0.435 μW, which is enough to axle box bolt tightness monitoring. Self-powered V-TENG sensor produced an open circuit voltage of 9 V and current of 0.32 μA. The output voltage of V-TENG sensor has a linear correlation with the vibration frequency, which could be applied to tightening or loosening monitoring effectively. Deep Convolutional Neural Networks (DCNN) algorithms are adopted to the tightness state recognition of bolts with the self-powered V-TENG sensors efficiently with 90.38% success recognition accuracy. Self-powered V-TENG sensors have excellent performance in the tightness detection and early fault diagnosis of train axle box bolts, which also has great potential in the field of structural health monitoring (SHM) for trains, enhancing the safety and reliability of train operations.
研究领域:
机械工程
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智能传感
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机器学习
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铁路安全
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DOI
高影响因子
CHEMICAL ENGINEERING JOURNAL
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2024
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IF 13.3
Chengbin Jiang,Alan J.X. Guo,Yuwen Li,Yufan Wang,Jianfeng Sun,Zhizhou Chen,Xin Chen,Qianyun Ma
Colorimetric sensor arrays (CSAs) can detect the volatile organic compounds (VOCs) produced during food storage, enabling odor visualization for freshness monitoring. Herein, sixteen chemo-responsive dyes that undergo observable color changes to VOCs are absorbed on ammonium quaternized cellulose nanofibres (C-CNFs) to design a paper-based CSA. The strong ionic interactions between the dyes and C-CNFs immobilize the dyes onto the sensor without leakage and provide stable and consistent colorimetric change even at a high relative humidity (RH = 60 %). Owing to the three-dimensional porous structure of C-CNFs, the CSA demonstrates high sensitivity for detecting VOCs produced during shrimp and fish spoilage. The limits of detection for ammonia, dimethylamine, trimethylamine, cadaverine, and putrescine are 2, 21, 3, 1, and 1 ppm, respectively. By selecting dyes covering a wide range of VOCs, the color changes in the CSA can accurately differentiate fresh, less fresh, slightly spoilt, and spoilt shrimp and fish. These color changes are further digitized and used to train a convolutional neural network (CNN). The CNN method has high accuracy (99 %) for determining abovementioned freshness levels. The combination of CSA and CNN is a promising approach for fabricating a portable sensing platform for the advanced non-destructive determination of food freshness levels.
研究领域:
食品科学
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生物传感技术
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人工智能应用
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DOI
高影响因子
FOOD CHEMISTRY
·
2024
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IF 8.5
Shuai Liu,Jingkai Zhao,Junfeng Wu,Ling Wang,Chuanan Yao,Jiandong Hu,Hao Zhang
A novel approach combing a fluorescent microfluidic paper strip with a portable smartphone-based sensing platform is developed for rapid and sensitive detection of omethoate pesticide. The detection mechanism of the microfluidic paper strip is based on the fluorescence quenching of graphene oxide (GO) toward the cyanine 3 (Cy3)-labeled aptamer (Cy3-APT). Upon exposure to omethoate, the Cy3-APT detaches from the surface of GO, resulting in considerable fluorescence recovery, which can be visualized through the smartphone-based sensing platform. The images are analyzed through a self-developed app embedded with a pretrained convolutional neural network model, achieving a high regression coefficient of 0.9964 at an omethoate concentration range of 0–750 nM. The smartphone-based platform enables rapid on-site detection of omethoate pesticide in real samples within 10 min, with results comparable to those obtained using standard methods. In short, the proposed microfluidic paper-based fluorescent sensor combined with the smartphone-based sensing platform enhances the detection performance toward organophosphorus pesticides.
研究领域:
分析化学
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环境科学
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生物传感技术
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产品关联
DOI
PubMed
高影响因子
CHINESE JOURNAL OF CHEMISTRY
·
2021
·Vol. 40
·期 5
·609-616
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IF 6
Xiaoqing Tan,Yingying Ye,Hong Liu,Jianxin Meng,Lin-Lin Yang,Fengyu Li
Comprehensive Summary Biogenic amines (BAs) are important biomarkers for monitoring food quality and assisting in the diagnosis of disease. Facial, portable, accurate and high-throughput BAs detection is still challenging by the specific sensor compounds development or the complicated instrument operation. Deep learning (DL) algorithms are blooming for their superiority on the nonlinear and multidimensional data analysis, which endow the great advantage for the artificial intelligence assisted large sample analysis of the environmental or daily health monitoring. In this work, we developed a deep learning-assisted visualized fluorometric array-based sensing method. Two commercial fluorescent dyes were selected and combined into sensor arrays. Variation in the alkalinity of BAs causes significant and distinct fluorescence changes of the dyes. In conjunction with pattern recognition by the pretrained CNN models, the sensor array clearly differentiates seven BAs with 99.29% prediction accuracy and allows rapid single and multi-component quantification with a volume fraction range from 200 cm 3 /m 3 to 2500 cm 3 /m 3 . This method also provides a new way for meat freshness monitoring. We envision that this novel analytical method for BAs can be used as an alternative and promising tool for the detection of a wider variety of analytes.
研究领域:
食品科学
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分析化学
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人工智能
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环境监测
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健康监测
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产品关联
DOI
高影响因子
Journal of Innovative Optical Health Sciences
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2022
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IF 2.396
Xiao Ma,Honglian Xiong,Jinhao Guo,Zhiming Liu,Yaru Han,Mingdi Liu,Yanxian Guo,Mingyi Wang,Huiqing Zhong,Zhouyi Guo
Because the breast cancer is an important factor that threatens women’s lives and health, early diagnosis is helpful for disease screening and a good prognosis. Exosomes are nanovesicles, secreted from cells and other body fluids, which can reflect the genetic and phenotypic status of parental cells. Compared with other methods for early diagnosis of cancer (such as circulating tumor cells (CTCs) and circulating tumor DNA), exosomes have a richer number and stronger biological stability, and have great potential in early diagnosis. Thus, it has been proposed as promising biomarkers for diagnosis of early-stage cancer. However, distinguishing different exosomes remain is a major biomedical challenge. In this paper, we used predictive Convolutional Neural model to detect and analyze exosomes of normal and cancer cells with surface-enhanced Raman scattering (SERS). As a result, it can be seen from the SERS spectra that the exosomes of MCF-7, MDA-MB-231 and MCF-10A cells have similar peaks (939, 1145 and 1380 cm − 1 ). Based on this dataset, the predictive model can achieve 95% accuracy. Compared with principal component analysis (PCA), the trained CNN can classify exosomes from different breast cancer cells with a superior performance. The results indicate that using the sensitivity of Raman detection and exosomes stable presence in the incubation period of cancer cells, SERS detection combined with CNN screening may be used for the early diagnosis of breast cancer in the future.
研究领域:
癌症生物学
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生物医学工程
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医学诊断
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机器学习
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产品关联
DOI
高影响因子
ADVANCED FUNCTIONAL MATERIALS
·
2025
·
IF 19
Song Xiao,Haoying Wu,Haocheng Deng,Xiaoxing Zhang,Ju Tang,Yi Li
The advancement of the Industrial Internet of Things and digital-intelligent machinery presents considerable demands in detecting impurities in industrial equipment lubricants or oils. Herein, a Tubular oil–solid triboelectric nanogenerator (TOS-TENG) is designed as a self-powered sensor for impurity detection in insulating oil flow. Specifically, an oleophobic triboelectric sensing layer with superior electron trapping capability is created based on Nano silica and 1H,1H,2H,2H-perfluorodecyltriethoxysilane. Additionally, a periodic start-stop oil flow control method that facilitates the transition between flowing and stationary insulating oil is proposed, thereby generating periodic output signals from the TOS-TENG. Furthermore, a correlation between the output characteristics of the TOS-TENG and impurity properties (type, content) is established through a deep learning algorithm based on convolutional neural networks, enabling the identification of common impurities such as moisture, metal, and fiber particles in oil. As a proof of concept, an industrial-grade TOS-TENG-based device for in situ online monitoring of impurities in oil-immersed transformers is designed. This work offers novel insights into the compositional analysis of continuous fluids and provides a novel strategy for oil condition sensing in smart industrial equipment.
研究领域:
纳米技术
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传感器
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电气工程
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机器学习
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工业监测
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材料科学
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产品关联
DOI
高影响因子
Nano Energy
·
2025
·
IF 17.1
Wenqiu Liu,Meng Chen,Xiping Jiang,Wei Chen,Seng Zen,Ziyi Ren,Hengyu Guo,Hua Yu
As artificial intelligence technologies progress, Human-Machine Interactions (HMI) must evolve rapidly, necessitating reliable and continuous authentication solutions. We propose dynamic keystroke pattern recognition technology based on a piezoelectric-triboelectric coupling sensor array to address these challenges, enhancing the keystroke signal characteristics. Dual verification technology combining password and biometric authentication effectively enhances the security level of the Human-Machine Interactions system. The triboelectric sensor efficiently reduces the output channels of a 3 × 3 sensing array to a single channel using a mesh topology electrode design. Each of the nine triboelectric sensor units corresponds to nine numeric key, allowing users to input different key combinations that generate unique cryptographic waveforms distinguished by individual keystroke characteristics. A piezoelectric-triboelectric coupling sensor array, structured with multiple layers, is devised. By incorporating a piezoelectric sensor in the upper layer, we harness the complementary effects of piezoelectric and triboelectric properties to boost authentication accuracy and alleviate the limitations of single sensing modalities. Notably, crosstalk is eliminated through the specialized sensor array and the topological electrode design. Integrating the piezoelectric-triboelectric coupling sensor array with a 1D CNN neural network approach achieves password recognition accuracy surpassing 99%, effectively mitigating the risk of password leakage in systems facilitating human-computer interactions.
研究领域:
生物医学工程
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人工智能与安全
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传感器技术
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产品关联
DOI
高影响因子
危险品化学品经营许可证（带存储）