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One-Component Multichannel sensor array for Rapid Identification of Bacteria

ANALYTICAL CHEMISTRY 2022年第28期94卷 10291-10298页

作者： Wang, Hao Zhou, Lingjia Qin, Jiaojiao Chen, Jiahao Stewart, Callum Sun, Yimin Huang, Hui Xu, Lian Li, Linxian Han, Jinsong Li, Fei China Pharmaceut Univ Coll Engn Dept Food Qual & Safety State Key Lab Nat Med Nanjing 211109 Peoples R China China Pharmaceut Univ Coll Engn Natl R&D Ctr Chinese Herbal Med Proc Dept Food Qual & Safety Nanjing 211109 Peoples R China Karolinska Inst Ming Wai Lau Ctr Reparat Med S-17177 Stockholm Sweden China Pharmaceut Univ Sch Pharm Nanjing 211109 Peoples R China

Bacterial infections routinely cause serious problems to public health. To mitigate the impact of bacterial infections, sensing systems are urgently required for the detection and subsequent epidemiological control of pathogenic organisms. Most conventional approaches are time-consuming and highly instru ment-and professional operator-dependent. Here, we developed a novel one-component multichannel array constructed with complex systems made from three modified polyethyleneimine as well as negatively charged graphene oxide, which provided an information-rich multimode response to successfully identify 10 bacteria within minutes via electrostatic interactions and hydrophobic interactions. Furthermore, the concentration of bacteria (from OD600 = 0.025 to 1) and the ratio of mixed bacteria were successfully achieved with our smart sensing system. Our designed sensor array also exhibited huge potential in biological samples, such as in urine (OD600 = 0.125, 94% accuracy). The way to construct a sensor array with minimal sensor element with abundant signal outputs tremendously saves cost and time, providing a powerful tool for the diagnosis and assessment of bacterial infections in the clinic.

关键词： Coulomb's law Hydrophobe sensor array sensor Pathogenic bacteria

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Selection of Classifiers to Enhance Efficacy of Metal/Organic Hybrid sensor array for VOC and Toxic Gas Identification

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IEEE sensorS JOURNAL 2022年第20期22卷 19136-19143页

作者： Riek, Nathan T. So, Seth Akcakaya, Murat Yun, Minhee Univ Pittsburgh Dept Elect & Comp Engn Pittsburgh PA 15213 USA Taiwan Semicond Mfg Co TMSC Phoenix AZ 85021 USA

Modern developments in gas sensor technology include a decrease in size and an increase in sensitivity and selectivity. These improvements, paired with postprocessing tools, such as machine learning, are pushing gas detection toward viability for complex tasks, such as volatile organic compound (VOC) analysis in human breath. In our research, we use a sensor array fabricated in our lab featuring a hybrid combination of metals and organic polymers [palladium (Pd), zinc oxide (ZnO), polypyrrole (PPy), and polyaniline (PANI)] designed to detect a range of VOCs and toxic gases (CO, H2, CH3OH, and NO2). An exhaustive analysis of 25 machine learning classifiers using three different feature sets was completed to find the best classifier and feature set combinations for one versus rest gas classification. We determined that ensemble classifiers, using normalized sensor data as a feature set, yield the best classification results. From these results, we demonstrated that Pd, PPy, and PANI are best suited to identify H2, NO2, and CH3OH, respectively. Furthermore, PANI is best suited to identify CO, so we correctly identified four gases from three sensor materials with sensitivity values all above 85%. These promising classification results could allow us to expand our set of gases and, therefore, make this sensor array viable for real-world applications.

关键词： sensors sensor arrays Gas detectors Gases Zinc oxide sensor systems Sensitivity Gas sensor machine learning nanomaterial sensor array volatile organic compound (VOC)

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Temperature Compensation With Highly Integrated Ionization sensor array Used in Simultaneous Detection of Mixed Gases

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2022年第1期69卷 911-920页

作者： Cheng, Zhenzhen Zhang, Yong Muhammad, Waqas Hao, Yunhe Tang, Yujie Sun, Lili Wang, Di Zhou, Jintang Wang, Ruizhe Deng, Yuangang Li, Yunjia Geng, Li Han, Wen Xi An Jiao Tong Univ Sch Elect Engn State Key Lab Elect Insulat & Power Equipment Xian 710049 Peoples R China Xi An Jiao Tong Univ Sch Microelect Xian 710049 Peoples R China State Grid Shaanxi Elect Power Co Elect Power Res Inst Xian 710199 Peoples R China

On-line detection of fault characteristic gases released by the insulation oil of transformers is developed as a scientific basis for estimating explosion accidents. However, the performance of most gas sensors, such as H-2 and C2H2 sensors, is usually affected by temperature, and there exists large detection error when the temperature of transformer oil changes in operation. And the existing temperature compensation methods suffer from problems such as low integration, large volume, and high costs. Herein, a sensor array based on three triple-electrode sensors with carbon nanotubes was fabricated. Each of the three sensors has three vertically stacked electrodes with various interelectrode distances. The first and second sensors were used to detect H-2 and C2H2, respectively, and the third sensor was used to measure temperature. All the detection mechanisms are based on the dependence of discharge current on gas concentration and temperature. The sensor array could detect H-2/C2H2 mixture and temperature due to the exponential function between the output currents and electrode separations. The proposed sensor exhibited low relative and repeatability error values less than 5.1% and 4.9%, respectively, and displayed humidity compensation potential like temperature. High integration makes the sensor array superior among the other mechanism sensors.

关键词： Temperature sensors Temperature measurement sensor arrays Electrodes Current measurement Gases Power transformer insulation Gas discharge sensor array temperature compensation

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A chemiresistive sensor array based on polyaniline nanocomposites and machine learning classification

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BEILSTEIN JOURNAL OF NANOTECHNOLOGY 2022年 13卷 411-423页

作者： Kroutil, Jiri Laposa, Alexandr Ahmad, Ali Voves, Jan Povolny, Vojtech Klimsa, Ladislav Davydova, Marina Husak, Miroslav Czech Tech Univ Dept Microelect Tech 2 Prague 16627 Czech Republic Czech Acad Sci FZU Inst Phys Slovance 1999-2 Prague 18221 Czech Republic

The selective detection of ammonia (NH3), nitrogen dioxide (NO2), carbon oxides (CO2 and CO), acetone ((CH3)(2)CO), and toluene (C6H5CH3) is investigated by means of a gas sensor array based on polyaniline nanocomposites. The array composed by seven different conductive sensors with composite sensing layers are measured and analyzed using machine learning. Statistical tools, such as principal component analysis and linear discriminant analysis, are used as dimensionality reduction methods. Five different classification methods, namely k-nearest neighbors algorithm, support vector machine, random forest, decision tree classifier, and Gaussian process classification (GPC) are compared to evaluate the accuracy of target gas determination. We found the Gaussian process classification model trained on features extracted from the data by principal component analysis to be a highly accurate method reach to 99% of the classification of six different gases.

关键词： feature extraction gas sensor pattern recognition sensor array

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Development of a colorimetric sensor array based on monometallic and bimetallic nanoparticles for discrimination of triazole fungicides

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ANALYTICAL AND BIOANALYTICAL CHEMISTRY 2022年第18期414卷 5297-5308页

作者： Kalantari, Kimia Fahimi-Kashani, Nafiseh Hormozi-Nezhada, M. Reza Sharif Univ Technol Dept Chem Tehran 111559516 Iran Sharif Univ Technol Inst Nanosci & Nanotechnol Tehran *** Iran

Due to the widespread use of pesticides and their harmful effects on humans and wildlife, monitoring their residual amounts in crops is critically essential but still challenging regarding the development of high-throughput approaches. Herein, a colorimetric sensor array has been proposed for discrimination and identification of triazole fungicides using monometallic and bimetallic silver and gold nanoparticles. Aggregation-induced behavior of AgNPs, AuNPs, and Au-AgNPs in the presence of four triazole fungicides produced a fingerprint response pattern for each analyte. Innovative changes to the metal composition of nanoparticles leads to the production of entirely distinct response patterns that can be used for the detection and discrimination of triazoles. Pattern recognition methods, including linear discriminant analysis (LDA) and hierarchical cluster analysis, have been employed for the differentiation of triazoles in the concentration range of 0.1-0.55 mu g mL(-1). Besides, the sensor array demonstrates promising practicability to satisfactorily distinguished triazole in mixtures and complex media of wheat flour and cucumber samples. The proposed colorimetric sensor array might pave the way towards a cost-effective and rapid, yet sensitive platform for high-throughput monitoring of residual amounts of pesticides for on-site applications.

关键词： Colorimetric sensor array Triazole fungicides Aggregation Single nanoparticles Bimetallic nanoparticles

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Point-of-Care Immunoassay Based on a Multipixel Dual-Channel Pressure sensor array with Visual Sensing Capability of Full-Color Switching and Reliable Electrical Signals

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ANALYTICAL CHEMISTRY 2022年第38期94卷 13278-13286页

作者： Huang, Lingting Zeng, Ruijin Xu, Jianhui Tang, Dianping Fuzhou Univ Dept Chem Key Lab Analyt Sci Food Safety & Biol MOE & Fujian Prov Fuzhou 350108 Peoples R China

The point-of-care (POC) method with affordability and portability for the sensitive detection of biological substances is an emerging topic in rapid disease screening and personalized medicine. In this work, we demonstrated a diverse responsive platform based on a dual-channel pressure sensor (DCPS). The DCPS had a multilayer flexible architecture consisting of a photonic hydrogel with chromatic transitions and a piezoresistive pressure sensor as the electrical data transmission unit, both of which had the property of pressure-induced mechanical stimulus feedback. By incorporating a platinum nanoparticles-labeled detection antibody (PtNPs-dAb) into the sandwich-type immunoreaction for the target carcinoembryonic antigen (CEA, as a model analyte), gas decomposition could be triggered by the addition of hydrogen peroxide (H2O2) to induce a significant increase under pressure in a closed chamber. Meanwhile, the DCPS enabled an accurate electrical signal output, and the photonic hydrogel converted spatiotemporal stimuli into eye-readable colorations with string brilliance. In this way, the target concentration could be quantificationally related to the electrical response and intuitively perceived through visible color alterations. Under optimal conditions, a sensitive determination of CEA was performed in a detectable range of 0.3-60 ng/mL with a limit of detection (LOD) of 0.13 ng/mL. In addition, the proposed protocol had satisfactory selectivity, accuracy, and reproducibility. Furthermore, an array-based immunoassay device was fabricated to conceptually validate its application potential in high-throughput biomedical detection and inspire a dual-signal POC diagnostic platform in a friendly way for resource-limited settings.

关键词： sensor array Immunoassay Pressure sensor Point of care

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Passive Distributed sensor array Using Multiple RF Sensing Tags

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IEEE INTERNET OF THINGS JOURNAL 2022年第17期9卷 16128-16139页

作者： Shao, Zhuang Wen, Yumei Li, Ping Wang, Guoda Wang, Yao Han, Tao Ji, Xiaojun Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai 200240 Peoples R China

Radio-frequency (RF) sensing tags are emerging as the next-generation paradigm toward gathering context-awareness information related to all objects' physical phenomena for the future Internet of Things (IoT). However, the most attention has been given to realize the sensing functions of a single tag rather than the mutual influences among adjacent tags. It has been found that when multiple RF sensing tags are deployed adjacently, these tags may fail to function properly. Therefore, motivated by the common requirements of deploying multiple RF sensing tags in the IoT, the mutual influences among tags are analyzed. We believe that the mutual impedance of tag antennas, which changes with the antenna separations among adjacent RF sensing tags, affects the wireless links between the tags and reader. The mutual impedance is analyzed using the method of moments and is substituted into forward and backward links. The calculation results show that the influence of the antenna mutual impedance on the RF sensing tag takes the wavelength of RF waves as the period, fluctuates, and gradually diminishes as the separations between antennas increase. To validate the analysis, a sensor array is formed using multiple RF sensing tags integrated with magnetic sensors. The experimental results show that once the antenna separations between the tags exceed 1.5 wavelengths, the backscattering sensing data can be decoded, and the interrogation success rates reach 100%. Numerous sensor arrays can be deployed ubiquitously with convenience, providing a clustered solution toward distributed sensing for the IoT.

关键词： sensors Radio frequency Internet of Things Wireless communication Wireless sensor networks RFID tags Antennas Distributed sensing Internet of Things (IoT) radio-frequency (RF) sensing tags radio-frequency identification (RFID) sensor array

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A Carbon Nanotube sensor array for the Label-Free Discrimination of Live and Dead Cells with Machine Learning

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ANALYTICAL CHEMISTRY 2022年第8期94卷 3565-3573页

作者： Liu, Zhengru V. Shurin, Galina Bian, Long White, David L. Shurin, Michael R. Star, Alexander Univ Pittsburgh Dept Chem Pittsburgh PA 15260 USA Univ Pittsburgh Dept Pathol Med Ctr Pittsburgh PA 15261 USA Univ Pittsburgh Med Ctr Dept Immunol Pittsburgh PA 15213 USA Univ Pittsburgh Dept Bioengn Pittsburgh PA 15261 USA

Developing robust cell recognition strategies is important in biochemical research, but the lack of well-defined target molecules creates a bottleneck in some applications. In this paper, a carbon nanotube sensor array was constructed for the label-free discrimination of live and dead mammalian cells. Three types of carbon nanotube field-effect transistors were fabricated, and different features were extracted from the transfer characteristic curves for model training with linear discriminant analysis (LDA) and support-vector machines (SVM). Live and dead cells were accurately classified in more than 90% of samples in each sensor group using LDA as the algorithm. The recursive feature elimination with cross-validation (RFECV) method was applied to handle the overfitting and optimize the model, and cells could be successfully classified with as few as four features and a higher validation accuracy (up to 97.9%) after model optimization. The RFECV method also revealed the crucial features in the classification, indicating the participation of different sensing mechanisms in the classification. Finally, the optimized LDA model was applied for the prediction of unknown samples with an accuracy of 87.5-93.8%, indicating that live and dead cell samples could be well-recognized with the constructed model.

关键词： Machine learning sensor array sensor Nanotube Carbon nanotube

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A Gold Nanorods Etching Based Colorimetric sensor array for the Detection of Reducing Substances and Discrimination of Chinese Baijiu

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FOOD ANALYTICAL METHODS 2022年第9期15卷 2605-2614页

作者： Li, Jiawei Liu, Xiaofang Luo, Huibo Zhao, Dong Zhang, Jing Qiao, Cailin Ma, Yi Huo, Danqun Hou, Changjun Chongqing Univ Postdoctoral Res Stn Chongqing 400044 Peoples R China Chongqing Univ Key Lab Biorheol Sci & Technol State & Local Joint Engn Lab Vasc Implants Minist EducBioengn Coll Chongqing 400044 Peoples R China Chongqing Univ Three Gorges Hosp Chongqing 404000 Peoples R China Sichuan Univ Sci & Engn Coll Bioengn Liquor Making Biol Technol & Applicat Key Lab Sic Zigong 643000 Peoples R China Wuliangye Grp Co Ltd Strong Flavor Baijiu Solid State Fermentat Key La Yibin 644007 Peoples R China Chongqing Univ Sch Microelect & Commun Engn Chongqing Key Lab Biopercept & Intelligent Inform Chongqing 400044 Peoples R China

Baijiu brings huge economic effects for Chinese light industry and needs to be effectively discriminated. Reducing substances are considered as the evaluation index of Baijiu quality and health efficacy. A new reducing substances evaluation-based method for Baijiu detection is feasible and urgent. Herein, we report a colorimetric sensor array based on gold nanorods (Au NRs), which relies on reducing substances in Baijiu to slow down the etching of the edges of Au NRs by oxides to discriminate reducing substances and Baijiu. The Delta Red-Green-Blue (RGB) values of the morphology changes of the Au NRs with four particle sizes were captured for subsequent deep pattern recognition and fingerprinting construction. There is a good linear relationship between Euclidean distance and ethyl hexanoate concentration in the range of 20-1200 mu M (R-2 = 0.976), with a detection limit down to 20 mu M. Because of great qualitative and quantitative capabilities for typical reducing substances, 16 brands of Baijiu were accurately discriminated with the high blind sample test accuracy rate (90.6%). What's more, provided sensor array exhibits considerable selectivity in Baijiu samples and non-Baijiu samples, which indicated this method may have pleasing universality and practicability.

关键词： Gold nanorods Etching-based colorimetric sensor array Reducing substances discrimination Baijiu discrimination

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Online Learning for Active Odor Sensing Based on a QCM Gas sensor array and an Odor Blender

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IEEE sensorS JOURNAL 2022年第23期22卷 22310-22318页

作者： Aleixandre, Manuel Nakamoto, Takamichi Tokyo Inst Technol Lab Future Interdisciplinary Res Sci & Technol Yokohama Kanagawa 2260026 Japan Tokyo Inst Technol Inst Innovat Res Yokohama Kanagawa 2260026 Japan

In this work, a previously proposed procedure of active odor sensing was improved. During this active sensing measurement procedure, an odor blender was controlled to reproduce accurately the target odor of a previously measured blended odor. The final blended odor was the result of the active sensing measurement procedure. The odor was measured by a sensor array composed of four quartz crystal microbalances (QCMs) coated with different sensing films to obtain frequency changes and resistance changes. The odor blender mixed two volatile organic compounds, ethyl valerate and propionic acid. The procedure was robust to some degree against sensor drift or changes of ambient conditions such as humidity because of relative measurement. However, a recalibration was needed under changing environment. In this article, we present an improvement of the procedure with online learning of the parameters that avoid recalibrations. During the active measurement, the model parameters are updated by recursive least squares and the control loop is redesigned. This procedure was tested in different scenarios, sensor drift, and humidity. The results show that the system can adapt itself to sensor parameter changes improving the robustness of the active sensing procedure using the online learning.

关键词： Active sensing adaptive control drift compensation electronic nose gas sensor humidity compensation multi-input-multioutput (MIMO) control odor blender quartz crystal microbalance (QCM) gas sensor sensor array

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