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Inclined Crack Quantification of Plate-Like Structures Based on Circular sensor array and Lamb Waves

ACOUSTICAL PHYSICS 2024年第3期70卷 560-569页

作者： Wang, Z. Yan, Y. Gao, Z. Shi, B. Southeast Univ Natl & Local Unified Engn Res Ctr Basalt Fiber Pro Key Lab C & PC StructMinist Educ Natl & Local Unified Engn Nanjing 211189 Peoples R China Rocket Force Univ Engn Dept Basic Educ Xian 710025 Peoples R China

Cracks are one of the most common damages to thin plates. Therefore, rapid and accurate detection of cracks is of great significance in the nondestructive testing of thin plates. Most existing nondestructive testing technologies focus on crack localization rather than the quantification of the crack length and orientation. Therefore, this paper proposes a damage index, considering the wave attenuation during propagation, to quantify the crack length and orientation. Lamb waves are transmitted and received by a circular sensor array. Based on the abnormal reflection of the A0 mode of Lamb waves, the reflections at cracks are extracted by the Hilbert transform. The influence of the crack length and orientation are studied parametrically, and the relations between the proposed damage index and crack length and angle are given based on regression analysis.

关键词： Lamb wave crack quantification crack orientation sensor array inclined cracks

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Developing a Chemiresistive Gas sensor array for Simultaneous Detection of Ammonia and Carbon Dioxide Gases

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ACS sensorS 2024年第6期9卷 2836-2845页

作者： Rath, Ronil J. Zhang, Wesley B. Kavehei, Omid Dehghani, Fariba Naficy, Sina Farajikhah, Syamak Oveissi, Farshad Univ Sydney Sch Chem & Biomol Engn Sydney NSW 2006 Australia Univ Sydney Sch Biomed Engn Sydney NSW 2006 Australia Univ Sydney Sydney Nano Inst Sydney NSW 2006 Australia

Chemiresistive polymer-based sensors are promising platforms for monitoring various gases and volatile organic compounds. While they offer appealing attributes, such as ease of fabrication, flexibility, and cost-effectiveness, most of these sensors have a nearly identical response to cross-reactive gases, such as ammonia (NH3) and carbon dioxide (CO2). Aiming to address the shortcomings of chemiresistive polymer-based sensors in selectivity and simultaneous measurements of cross-reactive gases, a chemiresistive sensor array was developed consisting of components sensitive to carbon dioxide and ammonia as well as a control segment to provide the baseline. The designed system demonstrated a wide detection range for both ammonia (ranging from 0.05 to 1000 ppm) and carbon dioxide (ranging from 10(3) to 10(6) ppm) at both room and low temperatures (e.g., 4 degrees C). Our results also demonstrate the ability of this sensor array for the simultaneous detection of carbon dioxide and ammonia selectively in the presence of other gases and volatile organic compounds. Finally, the array was used to monitor CO2/NH3 in real food samples to demonstrate the potential for real-world applications.

关键词： simultaneous sensing ammonia sensor carbondioxide sensor sensor array chemiresistive sensorarray polymer-based sensing

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Fluorescence sensor array based on covalent organic frameworks and QSAR study for identification of organic pesticides

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MICROCHEMICAL JOURNAL 2025年 210卷

作者： An, Fangxia Li, Fang Deng, Shengyuan Wan, Ying Han, Haikang Luo, Jun Zhu, Longyi Qian, Hua Nanjing Univ Sci & Technol Sch Chem & Chem Engn Nanjing 210094 Peoples R China China Natl Work Safety Adm Testing Ctr Ind Explos Nanjing 210094 Peoples R China Nanjing Univ Sci & Technol Sch Environm & Biol Engn Nanjing 210094 Peoples R China Nanjing Univ Sci & Technol Sch Mech Engn Nanjing 210094 Peoples R China

Accurate identification of organic pesticides (OPs) is of great importance in agriculture, food safety and environmental protection. Due to the increasing number and greatly diversified structures, developing a rapid and universal strategy for OPs identification still remains a challenge. Herein, a fluorescence sensor array based on covalent organic frameworks (COFs) is designed to identify different types of OPs. The topologies of COFs are rationally designed by customizing the monomer and linkage structures to construct the fluorescence sensor array. Twenty-eight OPs with different core structures, including phenyl rings, five-membered heterocyclics, fused- and mixed-rings, are unambiguously discriminated ranging from 5 mu M to 1 mM. The resulting sensor array exhibits excellent selectivity against a variety of interferents. Importantly, the mechanism of the sensor array is investigated by quantitative structure-activity relationships (QSAR). Multiple linear regression (MLR) models are established to evaluate and predict the Stern-Volmer constants and binding constants, revealing direct connections between the fluorescence signals and OP structures. Eleven molecular descriptors feature the determining factors for the signal outputs. The QSAR analysis contributes to the development of sensor arrays and provides a novel approach to multivariate analysis.

关键词： Covalent organic frameworks Quantitative structure-activity relationships Fluorescence sensor array Organic pesticides

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Machine learning-driven sensor array based on luminescent metal-organic frameworks for simultaneous discrimination of multiple anions

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CHEMICAL ENGINEERING JOURNAL 2025年 512卷

作者： Wei, Dali Xu, Cheng Wang, Ying Feng, Weiwei Deng, Chunmeng Wu, Xiangyang Deng, Yibin Yang, Zhugen Zhang, Zhen Jiangsu Univ Sch Environm & Safety Engn Sch Emergency Management Zhenjiang 212013 Peoples R China Youjiang Med Univ Nationalities Ctr Med Lab Sci Affiliated Hosp Baise 533000 Peoples R China Key Lab Clin Mol Diag & Res High Incidence Dis Wes Baise 533000 Guangxi Peoples R China Cranfield Univ Sch Water Energy & Environm Milton Keynes MK43 0AL England

Due to the high correlation of anions in waters to environmental quality and human health, thus there is urgent need for developing simple and effective sensors to discriminate multiple anions. Herein, a machine learningassisted fluorescent sensor array based on two luminescent metal-organic frameworks (LMOFs, UiO-66-NH2 and UiO-66-OH) was developed for simultaneous discrimination of five anions (F-, PO43- , ClO44- , NO3- , and SO42- ). Wherein, UiO-66-NH2 and UiO-66-OH were designed by anchoring 2,5-diaminoterephthalic acid and 2,5dihydroxyterephthalic acid on UiO-66, respectively, which exhibited blue and green fluorescence emission, possessing good fluorescence property. Interestingly, the anions could effectively enhance the fluorescence intensity of UiO-66-NH2 and UiO-66-OH to generate diverse fluorescence responses and unique fingerprints, which could be utilized to develop a fluorescence sensor array for the rapid identification of five anions. Under the optimized conditions, the proposed sensor array showed good performance for identifying multiple anions and their mixtures with satisfactory sensitivity. More importantly, the integration of machine learning algorithm and sensor array has successfully achieved accurate identification and prediction of five anions in real water samples, affirming its practicability in actual samples. Our findings provided a promising tool for detecting multiple anions, and inspired potentials of the combination of sensor arrays and machine learning algorithm for pollution control in real waters.

关键词： Anions detection sensor array Metal-organic frameworks Environmental monitoring

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Development of Multichannel Quartz Tuning Fork sensor array System for the Detection of Volatile Organic Compounds

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IEEE sensorS JOURNAL 2024年第9期24卷 15082-15090页

作者： Vishwakarma, Satyendra Ray, Bishakha Krishna, Aarathi G. Datar, Suwarna Deemed Univ Def Inst Adv Technol Dept Appl Phys Pune 411025 Maharashtra India

The rapid advancement in electronic technology has ushered in a new era for sensor array systems, particularly within the realm of electronic olfaction, often called "electronic nose." These sophisticated devices harness various sensors, each with unique responses to diverse gases, and amalgamate their data into distinctive gas "finger-prints" with the help of Artificial Intelligence (AI) and machine learning (ML) algorithms. In the present research we have developed a portable sensor array system endowed with heightened sensitivity and selectivity, ideally suited for field applications in volatile organic compound (VOC) detection. This system comprises three core components: the sensor unit, the sensor support circuitry, and the processing unit. Our approach incorporates polymer modified Quartz Tuning Forks (QTFs) as sensors. Two systems were developed: the first using a microcontroller-based system having 4 sensors in the array and the second employing a Raspberry Pi (RPi)-based system incorporating 12 sensors. Various tests were conducted to evaluate these systems and compared with the NI DAQ system used for data acquisition (DAQ), which has an excellent signal-to-noise ratio (SNR). The SNR was compared among these three systems, taking into account the number of input channels. We conducted tests using nine different gases with a maximum of 8 different sensors in the array: acetone, ethanol, acetaldehyde, ammonia, octane, decane, isoprene, methanol, and isopropanol, to produce fingerprint output for each which will be used in ML for classification.

关键词： sensors sensor arrays sensor systems Pins Oscillators Microcontrollers Signal to noise ratio Electronic nose microcontroller-based system QTF Pi HAT circuit quartz tuning fork (QTF) sensor Raspberry Pi (RPi)-based system sensor array signal-to-noise ratio (SNR) volatile organic compounds (VOCs)

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Metal oxide-based gas sensor array for VOCs determination in complex mixtures using machine learning

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MICROCHIMICA ACTA 2024年第4期191卷 196-196页

作者： Singh, Shivam Sajana, S. Varma, Poornima Sreelekha, Gajje Adak, Chandranath Shukla, Rajendra P. Kamble, Vinayak B. Indian Inst Sci Educ & Res Sch Phys Thiruvananthapuram 695551 Kerala India Indian Inst Informat Technol Dept CSE Lucknow 226002 Uttar Pradesh India Indian Inst Technol Dept CSE Patna 801106 Bihar India Univ Twente BIOS Lab On a Chip Grp MESA Inst Nanotechnol Max Planck Ctr Complex Fluid Dynam POB 217 NL-7500 Enschede Netherlands

Detection of volatile organic compounds (VOCs) from the breath is becoming a viable route for the early detection of diseases non-invasively. This paper presents a sensor array of 3 component metal oxides that give maximal cross-sensitivity and can successfully use machine learning methods to identify four distinct VOCs in a mixture. The metal oxide sensor array comprises NiO-Au (ohmic), CuO-Au (Schottky), and ZnO-Au (Schottky) sensors made by the DC reactive sputtering method and having a film thickness of 80-100 nm. The NiO and CuO films have ultrafine particle sizes of < 50 nm and rough surface texture, while ZnO films consist of nanoscale platelets. This array was subjected to various VOC concentrations, including ethanol, acetone, toluene, and chloroform, one by one and in a pair/mix of gases. Thus, the response values show severe interference and departure from commonly observed power law behavior. The dataset obtained from individual gases and their mixtures were analyzed using multiple machine learning algorithms, such as Random Forest (RF), K-Nearest Neighbor (KNN), Decision Tree, Linear Regression, Logistic Regression, Naive Bayes, Linear Discriminant Analysis, Artificial Neural Network, and Support Vector Machine. KNN and RF have shown more than 99% accuracy in classifying different varying chemicals in the gas mixtures. In regression analysis, KNN has delivered the best results with an R2 value of more than 0.99 and LOD of 0.012 ppm, 0.015 ppm, 0.014 ppm, and 0.025 ppm for predicting the concentrations of acetone, toluene, ethanol, and chloroform, respectively, in complex mixtures. Therefore, it is demonstrated that the array utilizing the provided algorithms can classify and predict the concentrations of the four gases simultaneously for disease diagnosis and treatment monitoring.

关键词： Gas sensor sensor array Metal oxides VOCs Complex mixture Machine learning

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Research on Gas Recognition Based on Gas sensor array and Feature Analysis

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IEEE sensorS JOURNAL 2024年第15期24卷 24958-24971页

作者： Quan, Hao Sun, Jianhai Zhou, Tianye Chen, Tingting Niu, Zhiyuan Ma, Xibo Chinese Acad Sci Aerosp Informat Res Inst State Key Lab Transducer Technol Beijing 100194 Peoples R China Univ Chinese Acad Sci UCAS Sch Elect Elect & Commun Engn Beijing 100049 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Multimodal Artificial Intelligence S Beijing 100190 Peoples R China

In this work, a sensor array consisting of four sensors is designed. Detection of any mixture concentration in the range of 0-450 ppm hydrogen, 0-450 ppm ethylene, and 0-450 ppm carbon monoxide were achieved. Filter processing reduces the influence of sensor noise and ensures high recognition accuracy. Then, a total of 48 features such as response value, time feature, and first- and second-order features of sensor resistance were extracted from four sensors in each sample. In this article, the first- and second-order features are creatively proposed, which have a significant impact on the accuracy of the later model. It is found that gradient boosting decision tree (GBDT) has the highest classification accuracy, reaching 100%. Compared to models trained using only the first 180 s of data, gas prediction can be achieved at the initial sensor response stage, and the GBDT model has a preparation rate of up to 99.64%. To study the influence of different features on the sensor, the feature contribution of selected features in GBDT method is described. For the classification tasks, response time, recovery time, and second-order features contribute the most. Finally, the regression designed by GBDT method has a root-mean-square error of 23.52 ppm and an accuracy of 94.77%. This method did not require a lot of calculation and was suitable for running in small-embedded devices.

关键词： sensors Gas detectors sensor arrays Thermal sensors Batteries Feature extraction Support vector machines MEMS mixed gas analysis neural networks pattern recognition sensor array

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Polyurethane Foam Embedded Pressure sensor array With 3-D Printed Structure and Thin MEMS Strain Gauge for Car Seat

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IEEE sensorS JOURNAL 2024年第24期24卷 40595-40602页

作者： Sato, Suguru Nakagawa, Junya Yamamoto, Michitaka Takamatsu, Seiichi Kobayashi, Takeshi Itoh, Toshihiro Univ Tokyo Sch Engn Dept Precis Engn Tokyo 1138656 Japan Natl Inst Adv Ind Sci & Technol Tsukuba Ibaraki 3058564 Japan

We have developed a car seat with a pressure sensor array embedded under polyurethane (PU) foam to monitor the posture and driving behavior of the drivers. Each pressure sensor consists of a 5-mu m-thick piezoresistive micro-electromechanical systems (MEMSs) strain gauge wired on a 3-D printed parallel-plate cantilever. A cover is attached on top of the cantilever pressure sensor to concentrate the pressure applied through the PU foam, and it is surrounded by a plastic frame of the same height to prevent geometric nonlinear pressure sensor characteristics. To monitor drivers, the proposed 4 x 4 pressure sensor array is embedded under the PU foam of a car seat. The pressure sensor has a sensitivity range of 0-20 kPa and a determination coefficient of the sensor's regression line of 0.99, which exhibits high linearity. Our evaluation using the prototype sensor array and a driving simulator demonstrated that driver posture changes and braking behavior could be detected. The sensor array is expected to be applied to smart car seats for automated vehicles in the future.

关键词： sensors sensor arrays Pressure sensors Automobiles Vehicles Strain measurement Micromechanical devices Three-dimensional printing Silicon Monitoring Driver monitoring hyperelastic foam packaging structure sensor array

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Metal-organic frameworks-based nanozyme sensor array for the discrimination of biogenic amines and detection of histamine

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TALANTA 2025年 284卷 127244页

作者： Zou, Jiahui Zhu, Guancheng Lin, Xueer Chu, Lanling Zhong, Huimin Jiang, Cong Huang, Yanyan Nanjing Forestry Univ Coll Light Ind & Food Engn Nanjing 210037 Peoples R China

Biogenic amines (BAs) are hazardous substances found in fishery products that are closely associated with fish spoilage and threaten food safety. Traditional chromatographic methods for detecting BAs are expensive, complex, and time-consuming. In this study, we developed a nanozyme-based sensor array to efficiently discriminate between four types of BAs and sensitively detect histamine. Copper-, cerium-, and manganese-based metal-organic frameworks with excellent peroxidase-like activities were employed as sensor elements. Because the catalytic activities of metal-organic frameworks could be modified by different BAs to varying degrees, the sensor array could generate a distinct colorimetric response pattern (fingerprint) for each BA. Based on this principle, the sensor array accurately discriminated BAs over a wide concentration range (10-1000 mu M). Histamine could be distinguished down to 1 mu M and detected with a detection limit of 4.28 mu M within 20 min. In addition, mixtures of BAs, target BAs, interfering substances, and BAs in fishery product samples were well discriminated. Furthermore, our sensor array could also effectively distinguish the freshness of fish samples. This work might offer a useful strategy for the discrimination and detection of BAs and could positively contribute to food safety and public health.

关键词： Metal-organic frameworks Nanozyme sensor array Biogenic amines Histamine

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Multicolorimetric sensor array Based on Silver Metallization of Gold Nanorods for Discriminating Dopaminergic Agents

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ACS CHEMICAL NEUROSCIENCE 2024年第19期15卷 3513-3524页

作者： Koohsar, Reza Orouji, Afsaneh Hormozi-Nezhad, Mohammad Reza Sharif Univ Technol Dept Chem Tehran 111559516 Iran

Dopaminergic agents are compounds that modulate dopamine-related activity in the brain and peripheral nerves within the pathways on both sides of the blood-brain barrier. Atypical levels of them can precipitate a multitude of neurological disorders, whose timely diagnosis signifies not only stopping the advancement of the illness but also surmounting it. A silver metallized gold nanorod (AuNRs) conditional sensor array, designed to detect dopaminergic agents for assessing nervous system disorders, yielded significant results in simultaneous detection and discrimination of Benserazide (Benz), Levodopa (L-DOPA), and Carbidopa (Carb). The array was composed of two different concentrations of silver ions as sensor elements (SEs), which generated unique signatures indicative of the presence of reductive target analytes, triggered by the incongruent formation of the Au@Ag core-shell, causing visual and fingerprint colorimetric patterns. Generating diverse responses is the key to the functionality of array-based sensing, which facilitated achieving spectral and color variation originating from the blue shift of AuNRs longitudinal localized surface plasmon resonance (LLSPR) in the extinction spectrum. Also, employing a smartphone camera enables clear visual discrimination across an extensive concentration span. Pattern recognition through linear discriminant analysis (LDA) underscored the robust discrimination accuracies of this sensor, along with quantification by means of partial least-squares regression (PLSR), affirming its potential for practical applications. Notably, the array demonstrated high sensitivity in detecting varied concentrations of target analytes, even in commercial drug samples. The sensor responses exhibited a linear correlation with the concentrations of Benz, L-DOPA, and Carb ranging from 1.59 to 100.0, 5.26 to 100.0, and 5.32 to 100.0 mu mol L-1, respectively, and the minimum detectable concentrations for Benz, L-DOPA, and Carb were measured at 0.53

关键词： silver metallization gold nanorods dopaminergicagents sensor array machine learning

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