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A Combined Magnetoelectric sensor array and MRI-Based Human Head Model for Biomagnetic FEM Simulation and sensor Crosstalk Analysis

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sensorS 2024年第4期24卷 1186页

作者： Oezden, Mesut-Oemuer Barbieri, Giuseppe Gerken, Martina Univ Kiel Dept Elect & Informat Engn Integrated Syst & Photon Kaiserstr 2 D-24143 Kiel Germany

Magnetoelectric (ME) magnetic field sensors are novel sensing devices of great interest in the field of biomagnetic measurements. We investigate the influence of magnetic crosstalk and the linearity of the response of ME sensors in different array and excitation configurations. To achieve this aim, we introduce a combined multiscale 3D finite-element method (FEM) model consisting of an array of 15 ME sensors and an MRI-based human head model with three approximated compartments of biological tissues for skin, skull, and white matter. A linearized material model at the small-signal working point is assumed. We apply homogeneous magnetic fields and perform inhomogeneous magnetic field excitation for the ME sensors by placing an electric point dipole source inside the head. Our findings indicate significant magnetic crosstalk between adjacent sensors leading down to a 15.6% lower magnetic response at a close distance of 5 mm and an increasing sensor response with diminishing crosstalk effects at increasing distances up to 5 cm. The outermost sensors in the array exhibit significantly less crosstalk than the sensors located in the center of the array, and the vertically adjacent sensors exhibit a stronger crosstalk effect than the horizontally adjacent ones. Furthermore, we calculate the ratio between the electric and magnetic sensor responses as the sensitivity value and find near-constant sensitivities for each sensor, confirming a linear relationship despite magnetic crosstalk and the potential to simulate excitation sources and sensor responses independently.

关键词： biomagnetic sensor crosstalk finite-element method (FEM) human head model magnetic fields magnetoelectric effect MRI data multiferroic device multiscale model sensor array

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Surface-mediated fluorescent sensor array for identification of gut microbiota and monitoring of colorectal cancer

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TALANTA 2024年 274卷 126081页

作者： Liu, Zhihui Zeng, Meizi Xiao, Yuquan Zhu, Xiaohua Liu, Meiling Long, Ying Li, Haitao Zhang, Youyu Yao, Shouzhuo Hunan Normal Univ Coll Chem & Chem Engn Key Lab Chem Biol & Tradit Chinese Med Res Minist Educ Changsha 410081 Peoples R China Cent South Univ Hunan Canc Hosp Affiliated Canc Hosp Xiangya Sch Med Changsha 410013 Peoples R China

The development of efficient, accurate, and high-throughput technology for gut microbiota sensing holds great promise in the maintenance of health and the treatment of diseases. Herein, we developed a rapid fluorescent sensor array based on surface-engineered silver nanoparticles (AgNPs) and vancomycin-modified gold nanoclusters (AuNCs@Van) for gut microbiota sensing. By controlling the surface of AgNPs, the recognition ability of the sensor can be effectively improved. The sensor array was used to successfully discriminate six gut-derived bacteria, including probiotics, neutral, and pathogenic bacteria and even their mixtures. Significantly, the sensing system has also been successfully applied to classify healthy individuals and colorectal cancer (CRC) patients rapidly and accurately within 30 min, demonstrating its clinically relevant specificity.

关键词： Gut microbiota sensor array Surface-engineered Silver nanoparticles Gold nanoclusters

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Linear and Non-Linear Modelling Methods for a Gas sensor array Developed for Process Control Applications

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sensorS 2024年第11期24卷 3499页

作者： Lakhmi, Riadh Fischer, Marc Darves-Blanc, Quentin Alrammouz, Rouba Rieu, Mathilde Viricelle, Jean-Paul Univ Lyon Ctr SPIN Mines St Etienne CNRSUMR 5307 LGF F-42023 St Etienne France

New process developments linked to Power to X (energy storage or energy conversion to another form of energy) require tools to perform process monitoring. The main gases involved in these types of processes are H2, CO, CH4, and CO2. Because of the non-selectivity of the sensors, a multi-sensor matrix has been built in this work based on commercial sensors having very different transduction principles, and, therefore, providing richer information. To treat the data provided by the sensor array and extract gas mixture composition (nature and concentration), linear (Multi Linear Regression-Ordinary Least Square "MLR-OLS" and Multi Linear Regression-Partial Least Square "MLR-PLS") and non-linear (Artificial Neural Network "ANN") models have been built. The MLR-OLS model was disqualified during the training phase since it did not show good results even in the training phase, which could not lead to effective predictions during the validation phase. Then, the performances of MLR-PLS and ANN were evaluated with validation data. Good concentration predictions were obtained in both cases for all the involved analytes. However, in the case of methane, better prediction performances were obtained with ANN, which is consistent with the fact that the MOX sensor's response to CH4 is logarithmic, whereas only linear sensor responses were obtained for the other analytes. Finally, prediction tests performed on one-year aged sensor platforms revealed that PLS model predictions on aged platforms mainly suffered from concentration offsets and that ANN predictions mainly suffered from a drop of sensitivity.

关键词： sensor array Power to X multivariate analysis PLS ANN

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Hybrid 3DCNN-RBM Network for Gas Mixture Concentration Estimation With sensor array

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IEEE sensorS JOURNAL 2021年第21期21卷 24263-24273页

作者： Pareek, Vishakha Chaudhury, Santanu Singh, Sanjay CSIR Cent Elect Engn Res Inst Pilani 333031 Rajasthan India Acad Sci & Innovat Res AcSIR Ghaziabad 201002 India Indian Inst Technol Jodhpur Comp Sci & Engn Dept Karwar 342037 India CSIR Cent Elect Engn Res Inst Intelligent Syst Grp Pilani 333031 Rajasthan India

This work proposes a hybrid 3D Convolutional Neural Network and Restricted Boltzmann Machine (Hybrid 3DCNN-RBM) architecture tailored for gas concentration estimation. The immense success of deep learning in computer vision and natural language processing inspired us to design a deep-learning-based gas concentration estimation network. The proposed network is a joint 3DCNN and RBM network. It uses raw time-series gas sensor array data and provides the concentration of each gas in a mixture of gases. This raw time series data is considered similar to video signal comparing sensor array response as an image, which varies with time. We first time utilized 3DCNN for sensor array data processing and developed a joint network with RBM to design an end-to-end gas concentration estimation model. Although several pattern recognition-based methods have been applied to estimate the gas concentration of the gas mixtures, the effectiveness of these techniques enormously depends on the hand-created feature engineering. Nevertheless, the experiments show that the proposed method is an efficient method for estimating gas concentration in the gas mixture for sensor array data.

关键词： sensors sensor arrays Gas detectors Estimation Feature extraction Chemical sensors Temperature sensors sensor array 3DCNN RBM gas mixture

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Misalignment Error Suppression Between Host Frame and Magnetic sensor array

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2021年 70卷 1-7页

作者： Pang, Hongfeng Pan, Mengchun Qu, Wei Qiu, Lei Yang, Jun Li, YunTao Yang, Daolin Wan, Chengbiao Zhu, Xuejun Space Engn Univ Dept Elect & Opt Engn Beijing 101416 Peoples R China Natl Univ Def Technol Coll Mechatron Engn & Automat Changsha 410073 Peoples R China

Magnetic sensor array is usually designed for object detection and localization, where sensor coordinate is required to be consistent. Misalignment angles between coordinates should be calculated and calibrated. A new calibration method is proposed in order to calibrate the misalignment error between host frame and sensors. Host platform frame and perpendicular platform are used to obtain misalignment angles of sensor array. Experiment results show that it can estimate all angles of magnetic sensors and suppress misalignment error. Misalignment rms error is a reduction from 1109.7 to 27.6 nT, which means 2.48% indeed, thus improving the remote sensing accuracy of array which is unified to host frame coordinate.

关键词： Host frame coordinate misalignment error sensor array suppression

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Simultaneous Detection of Exosomal microRNAs Isolated from Cancer Cells Using Surface Acoustic Wave sensor array with High Sensitivity and Reproducibility

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MICROMACHINES 2024年第2期15卷 249-249页

作者： Han, Su Bin Lee, Soo Suk Soonchunhyang Univ Dept Pharmaceut Engn 22 Soonchunhyang Ro Asan 31538 Chungcheongnam South Korea

We present a surface acoustic wave (SAW) sensor array for microRNA (miRNA) detection that utilizes photocatalytic silver staining on titanium dioxide (TiO2) nanoparticles as a signal enhancement technique for high sensitivity with an internal reference sensor for high reproducibility. A sandwich hybridization was performed on working sensors of the SAW sensor array that could simultaneously capture and detect three miRNAs (miRNA-21, miRNA-106b, and miRNA-155) known to be upregulated in cancer. sensor responses due to signal amplification varied depending on the concentration of synthetic miRNAs. It was confirmed that normalization (a ratio of working sensor response to reference sensor response) screened out background interferences by manipulating data and minimized non-uniformity in the photocatalytic silver staining step by suppressing disturbances to both working sensor signal and reference sensor signal. Finally, we were able to successfully detect target miRNAs in cancer cell-derived exosomal miRNAs with performance comparable to the detection of synthetic miRNAs.

关键词： surface acoustic wave sensor array microRNA titanium oxide nanoparticles photocatalytic silver staining signal amplification

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Calixarene-Based Supramolecular sensor array for Pesticide Discrimination

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sensorS 2024年第12期24卷 3743页

作者： Chen, Yeye Tian, Jia-Hong Tian, Han-Wen Ma, Rong Wang, Ze-Han Pan, Yu-Chen Hu, Xin-Yue Guo, Dong-Sheng Nankai Univ Collaborat Innovat Ctr Chem Sci & EngnMinist Educ Frontiers Sci Ctr New Organ MatterKey Lab Funct State Key Lab Elemento Organ ChemColl Chem Tianjin 300071 Peoples R China

The identification and detection of pesticides is crucial to protecting both the environment and human health. However, it can be challenging to conveniently and rapidly differentiate between different types of pesticides. We developed a supramolecular fluorescent sensor array, in which calixarenes with broad-spectrum encapsulation capacity served as recognition receptors. The sensor array exhibits distinct fluorescence change patterns for seven tested pesticides, encompassing herbicides, insecticides, and fungicides. With a reaction time of just three minutes, the sensor array proves to be a rapid and efficient tool for the discrimination of pesticides. Furthermore, this supramolecular sensing approach can be easily extended to enable real-time and on-site visual detection of varying concentrations of imazalil using a smartphone with a color scanning application. This work not only provides a simple and effective method for pesticide identification and quantification, but also offers a versatile and advantageous platform for the recognition of other analytes in relevant fields.

关键词： pesticide sensor array supramolecular chemistry calixarene discrimination

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A sensor array based on a nanozyme with polyphenol oxidase activity for the identification of tea polyphenols and Chinese green tea

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BIOsensorS & BIOELECTRONICS 2024年 250卷 116056页

作者： Yang, Xiaoyu Zou, Bin Zhang, Xinjian Yang, Jie Bi, Zhichun Huang, Hui Li, Yongxin Jilin Univ Coll Food Sci & Engn Changchun 130025 Peoples R China Jilin Univ Coll New Energy & Environm Key Lab Water Resources & Aquat Environm Jilin Pro Key Lab Groundwater Resources & EnvironmMinist Ed Changchun 130021 Peoples R China

Green tea is popular among consumers because of its high nutritional value and unique flavor. There is often a strong correlation among the type of tea, its quality level and the price. Therefore, the rapid identification of tea types and the judgment of tea quality grades are particularly important. In this work, a novel sensor array based on nanozyme with polyphenol oxidase (PPO) activity is proposed for the identification of tea polyphenols (TPs) and Chinese green tea. The absorption spectra changes of the nanozyme and its substrate in the presence of different TPs were first investigated. The feature spectra were scientifically selected using genetic algorithm (GA), and then a sensor array with 15 sensing units (5 wavelengths x 3 time) was constructed. Combined with the support vector machine (SVM) discriminative model, the discriminative rate of this sensor array was 100% for different concentrations of typical TPs in Chinese green tea with a detection limit of 5 mu M. In addition, the identification of different concentrations of the same tea polyphenols and mixed tea polyphenols have also been achieved. Based on the above study, we further developed a facile and efficient new method for the category differentiation and adulteration identification of green tea, and the accuracy of this array was 96.88% and 100% for eight types of green teas and different adulteration ratios of Biluochun, respectively. This work has significance for the rapid discrimination of green tea brands and adulteration.

关键词： sensor array Green tea Nanozyme Polyphenol oxidase

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A temperature field reconstruction method for spacecraft leading edge structure with optimized sensor array

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JOURNAL OF INTELLIGENT MATERIAL SYSTEMS AND STRUCTURES 2021年第17期32卷 2024-2038页

作者： Sun, Yulin Zhang, Chao Ji, Hongli Qiu, Jinhao Nanjing Univ Aeronaut & Astronaut Coll Aerosp Engn 29 Yudao St Nanjing 210016 Jiangsu Peoples R China

Aimed at monitoring the aerodynamic heating on the spacecraft leading edge structure, a temperature field reconstruction method for curved surface using a distributed sensor array is proposed in this paper. Limited by the sensor number and system weight, a sparse sensor array is usually used to measure the temperatures at the discrete positions. Considering the majority of heat energy transfers on the leading edge surface rather than the hollow inside or thermal insulation material, the temperature field on the curved surface of leading edge structure can be reconstructed through inverse distance weighting (IDW) method with improved accuracy by introducing arc-length distances rather than straight-line distances. Furthermore, the non-dominated sorting genetic algorithm II (NSGA-II) is employed to optimize the distribution of the sensor array. Taking a three-dimensional spacecraft leading edge structure as an example, a finite element model is used to validate the proposed method. The influences of search radius and random initial population inputs on the optimized results are investigated. The results show that the use of search radius plays an important role in optimizing the arrangement of sensor array.

关键词： Reconstruction temperature field IDW optimization sensor array

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Fluorescent sensor array for Quantitative Determination of Saccharides

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ACS sensorS 2021年第11期6卷 4001-4008页

作者： Pushina, Mariia Penavic, Andrej Farshbaf, Sepideh Anzenbacher, Pavel, Jr. Bowling Green State Univ Dept Chem Bowling Green OH 43403 USA

Accurate monitoring of sugar levels is essential for many fields from food industry to human health. Here, we developed FRET-based dual chromophore sensors for saccharides that form oxazolidine boronate and may be employed as a noninvasive method for monitoring of sugar levels in biological fluids, namely, urine. The saccharide-binding properties of the sensors were studied using fluorescence spectroscopy and utilized in the determination of saccharides in a high-throughput manner. Here, two fluorescent sensors were successful in the classification of nine different monosaccharides and disaccharides with 100% correct classification. Furthermore, the dual chromophore self-assembled sensors were successfully utilized for the quantitative determination of important carbohydrates such as glucose in the presence of competitive saccharides (fructose) and in complex media (urine) without sample pretreatment. The present fluorescent sensors allow for quantification of glucose in a concentration range of 0-60 mM, which matches the concentration range of frequently used urinalysis test strips.

关键词： fluorescence saccharide sensing sensor array dual chromophore sensor systems urine analysis

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