ACS Applied Materials & Interfaces
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2024
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IF 8.3
Siyue Xiong,Chengxuan Wang,Chushu Zhu,Peitao Dong,Xuezhong Wu
Sweat, a noninvasive metabolic product of normal physiological responses, offers valuable clinical insights into body conditions without causing harm. Key components in sweat, such as urea and glucose, are closely linked to kidney function and blood glucose levels. Portable sweat sensors, equipped with diverse sensing systems, can monitor fluctuations in urea and glucose concentrations, thus providing methods for assessing kidney function and monitoring diabetes. This study presents a flexible, portable microfluidic surface-enhanced Raman scattering (SERS) sensor designed to detect the unique fingerprint of target biomarkers. This flexible, self-adhesive microfluidic chip, constructed from modified polydimethylsiloxane, features silver nanotripods (AgNTs) with densely distributed “hotspots” created via the oblique angle deposition technique. These AgNTs act as active substrates for SERS within the microfluidic platform, enabling direct skin contact to collect, transport, store, and analyze sweat. The chip functions as a quantitative urea sensor with a limit of detection (LOD) of 10–7 M. For enhanced sensitivity for glucose detection, the SERS substrate is modified with 4-mercaptophenylboronic acid, achieving a LOD of 10–7 M. This satisfies the measurement requirements for both urea and glucose in sweat under physiological conditions. Furthermore, the one-dimensional convolutional neural network model significantly enhances the accuracy of biomarker detection, facilitating the quantitative analysis of urea and glucose. This advancement contributes to the development of a controlled, convenient, and dynamic biosensing system for personalized point-of-care testing and supports the creation of intelligent wearable and nondestructive devices.
研究领域:
生物医学工程
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分析化学
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传感器技术
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DOI
PubMed
高影响因子
ANALYST
·
2022
·Vol. 147
·期 24
·5785-5795
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IF 5.227
Xuanyu Sha,Guoqiang Fang,Guangxu Cao,Shuzhi Li,Wuliji Hasi,Siqingaowa Han
Drug abuse is a global social issue of concern. As the drug market expands, there is an urgent need for technological methods to rapidly detect drug abuse to meet the needs of different situations. Here, we present a strategy for the rapid identification of benzodiazepines (midazolam and diazepam) using surface-enhanced Raman scattering (SERS) combined with neural networks (CNN). The method uses a self-assembled silver nanoparticle paper-based SERS substrate for detection. Then, a SERS spectrum intelligent recognition model based on deep learning technology was constructed to realize the rapid and sensitive distinction between the two drugs. In this work, a total of 560 SERS spectra were collected, and the qualitative and quantitative identification of the two drugs in water and a beverage (Sprite) was realized by a trained convolutional neural network (CNN). The predicted concentrations for each scenario could reach 0.1–50 ppm (midazolam in water), 0.5–50 ppm (midazolam in water and diazepam in Sprite), and 5–150 ppm (diazepam in Sprite), with a strong coefficient of determination (R2) larger than 0.9662. The advantage of this method is that the neural network can extract data features from the entire SERS spectrum, which makes up for information loss when manually identifying the spectrum and selecting a limited number of characteristic peaks. This work clearly clarifies that the combination of SERS and deep learning technology has become an inevitable development trend, and also demonstrates the great potential of this strategy in the practical application of SERS.
研究领域:
分析化学
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人工智能
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药物检测
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DOI
PubMed
高影响因子
SPECTROCHIMICA ACTA PART B-ATOMIC SPECTROSCOPY
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2024
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IF 3.2
Yujiang Gou,Xinglan Fu,Shilin Zhao,Panyu He,Chunjiang Zhao,Guanglin Li
Identification of soil contamination types is of great scientific significance for soil remediation and environmental pollution control. However, traditional identification methods for soil determination are time-consuming, laborious, and complicated. Here, we proposed an accurate method for identifying soil contamination types based on laser-induced breakdown spectroscopy (LIBS) and an improved convolutional neural network (CNN) model. The spectral feature extraction-based multiple attention residual network (SFEMARNet) model was constructed to extract detailed features by spectral feature extraction (SFE) modules, and highlight useful features by multiple attention residual (MAR) modules in LIBS spectral data. In addition, deep learning models and machine learning models were used to identify the data. The results showed that the SFEMARNet model achieved an accuracy of 98.75% on the test set. The recall, precision, and F1-score of the models reached 98.78%, 98.75%, and 98.76%, respectively, which were significantly better than the three deep learning models and of four machine learning models. It seems that the SFEMARNet model combined with LIBS technology may be a potential method for the accurate identification of soil contamination types.
研究领域:
环境科学
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人工智能
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土壤学
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DOI
高影响因子
Nano Energy
·
2024
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IF 16.8
Shixiang Wu,Hao Kan,Jianqiang Gao,Wenjing Yue,Chunwei Zhang,Song Gao,Yang Li
A multi-functional electronic skin (e-skin) with high sensitivity and wide response range is crucial for intelligent human-computer interaction (HCI) system. Here, a multi-functional hybrid e-skin (TPES) integrated by a piezo-capacitive pressure sensor (PCPS) and a triboelectric nanogenerator (TENG) is reported. An ion-gel electrospinning doped with MXene is combined with a micro-structured ion-gel to enrich the structural variations of the overall device, enabling TPES to achieve a high sensitivity of 616.42 kPa −1 , a broad response range of 0.6 Pa –1 MPa, and a fast response time of 5.6 ms. The vast surface area of the mixed cellulose foam layer effectively enhances the electrical output of the TENG, making TPES’s application scenarios become more flexible. By further integrating the different functionalities of the TPES with one-dimensional convolutional neural networks and HCI module, a gesture recognition and material perception HCI system is realized. Additionally, benefiting from the excellent performance of the PCPS and TENG, a multi-information recognition HCI system is established, capable of simultaneously recognizing the pressure and surface material of the balls and enabling real-time interaction with a virtual character. In summary, the proposed work offers a novel avenue for practical intelligent HCI system in virtual reality field.
研究领域:
材料科学
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人工智能
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生物医学工程
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DOI
高影响因子
SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY
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2025
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IF 4.6
Yan Yu,Wenjing Lu,Xiaobin Yao,Yurui Jiang,Junhui Li,Meng Yang,Xingjiu Huang,Xianghu Tang
Surface-enhanced Raman spectroscopy (SERS) is broadly used in the detection and analysis of materials with its fingerprint-like specificity and high sensitivity. However, resembling signals of analytes highly affect the identification and assignment of spectra, which has become a long-term issue to be solved. In this study, various models of machine learning are utilized and compared to support data analysis of complex SERS spectra. Silver-coated gold core–shell nanocubes (Au@AgNCs) are optimized as SERS substrates for the detection of four common dyes − methylene blue (MB), crystal violet (CV), rhodamine B (RhB) and malachite green (MG). Independent principal component analysis (ICA) was utilized to isolate the signals from the SERS spectra of the dye mixtures, and the isolated signals were further classified by commonly used classification models including K Nearest Neighbors (KNN), Support Vector Machines (SVM), Random Forests (RF), and Convolutional Neural Networks (CNN). The results show that the CNN model achieved an accuracy of 98% in the classification of single dyes and an accuracy of 97% in the classification of dye mixtures, which is significantly better than other models. Based on these findings, we propose ICA combined with CNN-assisted SERS spectroscopy as an effective analytical tool for analyzing dye mixtures.
研究领域:
分析化学
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光谱学
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人工智能
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DOI
PubMed
高影响因子
ACS Applied Materials & Interfaces
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2024
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IF 8.3
Wenjun Yan,Yun Liu,Yan Bai,Yulong Chen,Houpan Zhou,Waqar Ahmad
In this paper, we present the design and evaluation of an intelligent MEMS sensor employing the oxidized medium-entropy alloy (O-MEA) of FeCoNi as the gas-sensing material. Due to the specific catalytic exothermic reaction of the O-MEA on H2/CO, the sensor shows great selectivity for H2 and CO at 150 °C of the integrated microheater in the MEMS device, with the theoretical detection limit of 0.3 ppm for H2 and 0.29 ppm for CO. The MEMS heater, capable of instantaneous temperature changes in pulse operation mode, offers a novel approach for thermal modulation of the sensor, which is crucial for the adsorption and reaction of H2/CO molecules on the sensing layer surface. Consequently, we investigate the gas-sensing capabilities of the sensor under pulse heating modes (PHMs) of the MEMS heater and then propose the gas-sensing mechanism. The results indicate that PHMs significantly not only reduce the operating temperature and power consumption but also enhance the sensor’s functionality by providing multivariable response signals, paving the way for intelligent gas detection. Based on the high selectivity to H2 and CO, transforming the transient sensing curves into two-dimensional images via Gramian Angular Field (GAF) model and subsequent modeling using a convolutional neural network (CNN) algorithm, we have realized efficient and intelligent recognition of H2 and CO. This work provides insight for the development of low-power, high-performance MEMS gas sensors and further intelligence of individual MEMS sensors.
研究领域:
材料科学
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传感器技术
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人工智能
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产品关联
DOI
PubMed
高影响因子
INTERNATIONAL JOURNAL OF BIOLOGICAL MACROMOLECULES
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2024
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IF 7.7
Xia Xu,Xinyu Wang,Yicheng Ding,Xuxia Zhou,Yuting Ding
Preserving fish meat poses a significant challenge due to its high protein and low fat content. This study introduces a novel approach that utilizes a common type of lanthanide metal-organic frameworks (Ln-MOFs), EuMOFs, in combination with 5-fluorescein isothiocyanate (FITC) and methylcellulose (MC) to develop fluorescent sensor arrays for real-time monitoring the freshness of fish meat. The EuMOF-FITC/MC fluorescence films were characterized with excellent fluorescence response, ideal morphology, good mechanical properties, and improved hydrophobicity. The efficacy of the fluorescence sensor array was evaluated by testing various concentrations of spoilage gases (such as ammonia, dimethylamine, and trimethylamine) within a 20-min timeframe using a smartphone-based camera obscura device. This sensor array enables the real-time monitoring of fish freshness, with the ability to preliminarily identify the freshness status of mackerel meat with the naked eye. Furthermore, the study employed four convolutional neural network (CNN) models to enhance the performance of freshness assessment, all of which achieved accuracy levels exceeding 93 %. Notably, the ResNext-101 model demonstrated a particularly high accuracy of 98.97 %. These results highlight the potential of the EuMOF-based fluorescence sensor array, in conjunction with the CNN model, as a reliable and accurate method for real-time monitoring the freshness of fish meat.
研究领域:
食品科学
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材料科学
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人工智能
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DOI
PubMed
高影响因子
ACS Sensors
·
2024
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IF 8.2
Xiangxin Lin,Mingyu Cheng,Xinyi Chen,Jinglan Zhang,Yiping Zhao,Bin Ai
This study innovates plasmonic hydrogen sensors (PHSs) by applying phase space reconstruction (PSR) and convolutional neural networks (CNNs), overcoming previous predictive and sensing limitations. Utilizing a low-cost and efficient colloidal lithography technique, palladium nanocap arrays are created and their spectral signals are transformed into images using PSR and then trained using CNNs for predicting the hydrogen level. The model achieves accurate predictions with average accuracies of 0.95 for pure hydrogen and 0.97 for mixed gases. Performance improvements observed are a reduction in response time by up to 3.7 times (average 2.1 times) across pressures, SNR increased by up to 9.3 times (average 3.9 times) across pressures, and LOD decreased from 16 Pa to an extrapolated 3 Pa, a 5.3-fold improvement. A practical application of remote hydrogen sensing without electronics in hydrogen environments is actualized and achieves a 0.98 average test accuracy. This methodology reimagines PHS capabilities, facilitating advancements in hydrogen monitoring technologies and intelligent spectrum-based sensing.
研究领域:
材料科学
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人工智能
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传感技术
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DOI
PubMed
高影响因子
Advanced Intelligent Systems
·
2021
·Vol. 3
·期 4
·2000090
·
IF 0
Shengnan Chen,Heng Zhang,Xiangfei Yang,Xiaoguang Shao,Taihao Li,Na Chen,Zhenyi Chen,Wei Xue,Jiahua Pan,Shupeng Liu
Early diagnosis of prostate cancer (PCa) is always a great challenge in clinical practice, especially in distinguishing benign prostatic hyperplasia (BPH) from early cancer, due to the high similarity in pathology from the prostate-specific antigen (PSA) test and radiological detection. The conventional diagnostic methods are often less efficient in specificity and accuracy, leading to quite a few unnecessary biopsies. This work establishes a noninvasive diagnostic method for PCa by investigating urine samples using Raman spectroscopy and convolutional neural network (CNN) algorithm. The results of urine Raman spectra show the intensities of characteristic peaks for lipids, nucleic acids, and some amino acids are distinguishable between PCa and BPH, suggesting an abnormal metabolism caused by PCa, which can be detected by Raman spectroscopy. These data are then used to train an intelligent diagnostic model with CNN algorithm. The cross-validation results show the mean diagnostic accuracy, sensitivity, and specificity for PCa are 74.95%, 77.32%, and 72.46%, respectively. This noninvasive diagnostic method is a promising method for the early diagnosis of PCa, and the idea of using urine Raman spectroscopy with deep learning techniques for diagnosing PCa provides a reference for the application of artificial intelligence in the field of clinical medicine research.
研究领域:
肿瘤学
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泌尿科学
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分子诊断学
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转化医学
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生物光子学
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临床化学
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人工智能医学
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DOI
ANALYTICA CHIMICA ACTA
·
2021
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IF 6.558
Pengju Xing,Junhang Dong,Peiwen Yu,Hongtao Zheng,Xing Liu,Shenghong Hu,Zhenli Zhu
In this study, a simple and effective method for accurate determination of lithium in brine samples was developed by the combination of laser induced breakdown spectroscopy (LIBS) and convolutional neural network (CNN). Our results clearly demonstrate that the use of CNN could efficiently overcome the complex matrix effects, and thus allows for on-site Li quantitative determination in brine samples by LIBS. Specifically, two CNN models with different input data (M-CNN with matrix emission lines, and DP-CNN with double Li lines) were constructed based on the primary matrix features on spectrum and Boltzmann equation , respectively. It was observed that DP-CNN model could greatly improve the accuracy of Li analysis. We also compared the quantitative analysis capabilities of DP-CNN model with partial least squares regression (PLSR) and principal component analysis-support vector regression (PCA-SVR) model, and the results clearly showed DP-CNN offers the best quantification results (higher accuracy and less matrix interference). Finally, five real brine samples were successfully analyzed by the proposed DP-CNN model, confirming by the average absolute error of the prediction of 0.28 mg L −1 and the average relative error of 3.48%. These results clearly demonstrate that input data plays an important role in the training of CNN model in LIBS analysis, and the proposed DP-CNN provides an effective approach to solve the matrix effects encountered in LIBS for Li measurement in brine samples.
研究领域:
分析化学
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人工智能应用
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环境科学
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产品关联
DOI
PubMed
高影响因子
Nano Energy
·
2024
·
IF 16.8
Hao Zhang,Dongzhi Zhang,Ruiyuan Mao,Lina Zhou,Chunqing Yang,Yan Wu,Yukun Liu,Yuncheng Ji
Self-powered sensing technology and smart perception technology have broad application prospects in flexible and wearable electronics . In this work, a flexible triboelectric nanogenerator (RMP-TENG) based on a room-temperature vulcanized silicone rubber (RTV)@(Molybdenum disulfide (MoS 2 )/Polyvinyl chloride (PVC)) functional layer is developed using a layer-by-layer self-assembly and material doping strategy. The RTV@(MoS 2 /PVC) functional layer is divided into a charge-generating layer (RTV/PVC) and a charge-trapping layer (RTV/MoS 2 ). The synergistic effect of PVC and MoS 2 has improved the surface roughness and charge transfer efficiency of RMP-TENG, doubling its output performance to 1055 V and 112 μA. In order to further improve the tactile sensing accuracy of RMP-TENG, a deep learning model based on Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) is developed. It predicts the type of contact material based on the features of tactile signals, achieving a prediction accuracy of 93.975%. Additionally, by combining mobile smart terminals, the CNN-GRU model, and RMP-TENG, a wireless access control system based on self-powered tactile sensing and intelligent material recognition is developed. Through the optimization of these experiments and algorithms, RMP-TENG has achieved real-time material recognition capabilities. This demonstrates the broad application prospects of RMP-TENG in wearable energy supply, intelligent sensing, human-computer interaction, and other areas.
研究领域:
材料科学
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人工智能
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柔性电子学
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产品关联
DOI
高影响因子
SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY
·
2024
·
IF 4.3
Wenhui Li,Yingxin Chen,Xin Li,Yi Zhong,Pei Xu,Yuanjie Teng
Synthetic antioxidants serve as essential protectors against oxidation and deterioration of edible oils, however, prudent evaluation is necessary regarding potential health risks associated with excessive intake. The direct adsorption of antioxidants onto conventional surface-enhanced Raman scattering (SERS) substrates is challenging due to the presence of phenolic hydroxyl groups in their molecular structures, resulting in weak Raman scattering signals and rendering direct SERS detection difficult. In this study, a diazo derivatization reaction was employed to enhance SERS signals by converting antioxidant molecules into azo derivatives, enabling the amplification of the weak Raman scattering signals through the strong vibrational modes induced by the N = N double bond. The resulting diazo derivatives were characterized using UV–visible absorption and infrared spectroscopy, confirming the occurrence of diazo derivatization of the antioxidants. The proposed method successfully achieved the rapid detection of three commonly used synthetic antioxidants, namely butylated hydroxyanisole (BHA), tert -butylhydroquinone (TBHQ), and propyl gallate (PG) on interfacial self-assembled gold nanoparticles. Furthermore, rapid predictions of BHA, PG, and TBHQ within the concentration range of 1 × 10 −6 to 2 × 10 −3 mol/L were achieved by integrating a convolutional neural network model. The predictive range of this model surpassed the traditional quantitative method of manually selecting characteristic peaks, with linear coefficients (R 2 ) of 0.9992, 0.9997, and 0.9997, respectively. The recovery of antioxidants in real soybean oil samples ranged from 73.0 % to 126.4 %. Based on diazo derivatization, the proposed SERS method eliminates the need for complex substrates and enables the analysis and determination of synthetic antioxidants in edible oils within 20 min, providing a convenient analytical approach for quality control in the food industry.
研究领域:
分析化学
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食品科学
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生物医学工程
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产品关联
DOI
PubMed
高影响因子
危险品化学品经营许可证（带存储）