Article
Food Science & Technology
Xiaoguang Dong, Libing Gao, Haijun Zhang, Jing Wang, Kai Qiu, Guanghai Qi, Shugeng Wu
Summary: The study evaluated the sensory qualities of eggs from three commercial laying breeder strains, finding significant differences in smell profiles and successfully discriminating the breeds based on electronic nose data.
Article
Chemistry, Analytical
Juan C. Rodriguez Gamboa, Adenilton J. da Silva, Ismael C. S. Araujo, E. Eva Susana Albarracin, Cristhian M. A. Duran
Summary: Real-time gas classification is a crucial issue for various applications, and recent studies have shown that using SVM and DL models can effectively reduce response time while maintaining reliability. Experiments in this study demonstrated that the rapid detection approach has high potential, achieving reliable estimates with only a fraction of measurement data.
SENSORS AND ACTUATORS B-CHEMICAL
(2021)
Article
Multidisciplinary Sciences
Wen-li Zhang, Zhao-yu Liu, Kun Liang, Yi Wang, Ke-fan Chen, Yao-wei Sun, Sheng Wang
Summary: This paper introduces a novel visual gas sensing technology based on optical absorption gas sensing technology and spatial heterodyne spectroscopy. This technology presents invisible gas information in the form of a two-dimensional visual fingerprint and successfully detects NO2 in a laboratory environment for the first time. Experimental results show that this technology has good response to different spectra and concentrations of NO2, laying a foundation for gas sensing applications.
SCIENTIFIC REPORTS
(2022)
Article
Computer Science, Artificial Intelligence
Liuyuan Chen, Kanglei Zhou, Junchang Jing, Haiju Fan, Juntao Li
Summary: This work proposes a fast regularization parameter tuning algorithm for the twin multi-class support vector machine. By adopting a novel sample data set partition strategy and utilizing linear equations and block matrix theory, the regularization parameters are continuously updated, and the relationship between the Lagrangian multipliers and the regularization parameters is proven. Finally, different events are defined to seek for the starting event for the next iteration, and the effectiveness of the proposed method is validated through experiments.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Automation & Control Systems
Chuan Li, Diego Cabrera, Fernando Sancho, Mariela Cerrada, Rene-Vinicio Sanchez, Edgar Estupinan
Summary: This paper proposes an extension of OCSVM for fault severity discrimination in 3D printers, improving fault detection and severity discrimination through model optimization. Experimental results show that the distance to the hyperplane is informative for severity discrimination.
Article
Biochemistry & Molecular Biology
Ali Khorramifar, Mansour Rasekh, Hamed Karami, James A. Covington, Sayed M. Derakhshani, Jose Ramos, Marek Gancarz
Summary: This study investigated five potato varieties using an electronic nose and chemometric methods, measuring various parameters and establishing regression models. The results indicated specific relationships among different potato varieties, sugar, and carbohydrates. The accuracy in predicting sugar and carbohydrates varied among different potato varieties.
Article
Environmental Sciences
Roya Kolachian, Bahram Saghafian
Summary: Prediction of drought severity class/state using standardized hydrological drought index (SHDI) was conducted in this study. Results showed that considering drought classes as inputs/outputs leads to more accurate predictions, with SHDI3 prediction being more accurate than SHDI1 prediction. Rough set theory (RST) showed slightly better accuracy than support vector classification (SVC) and support vector regression (SVR) in forecasting.
ENVIRONMENTAL EARTH SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Wenwen Qiang, Hongjie Zhang, Jingxing Zhang, Ling Jing
Summary: The paper introduces a novel twin support vector machine, TSVM-M-3, for multi-class classification and a new RKT for large-scale classification. TSVM-M-3 considers the first and second-order moments of positive points loss and introduces an adjusting factor when constructing decision hyperplanes; RKT uses a density-dependent data selection method to reduce modeling error.
APPLIED SOFT COMPUTING
(2022)
Article
Chemistry, Analytical
Hongli Ma, Tao Wang, Bolong Li, Weiyang Cao, Min Zeng, Jianhua Yang, Yanjie Su, Nantao Hu, Zhihua Zhou, Zhi Yang
Summary: The novel quantification technique for electronic nose presented in this study, utilizing a double-step strategy combined with hierarchical classifier and partial least squares regression, demonstrates outstanding performance in identifying toxic gases and estimating concentrations. The approach is applicable for E-nose-based odor quantification.
SENSORS AND ACTUATORS B-CHEMICAL
(2022)
Article
Engineering, Electrical & Electronic
Wentian Zhang, Taoping Liu, Amber Brown, Maiken Ueland, Shari L. Forbes, Steven Weidong Su
Summary: The whisky market is prone to fraudulent activities, and it is challenging for most consumers to distinguish fraudulent beverages. A new electronic nose prototype called NOS.E has been developed to identify the differences between whiskies based on their odour. This study demonstrates the high accuracy of the proposed e-nose solution in brand name, region, and style classification, which was further validated using GC x GC-TOFMS.
IEEE SENSORS JOURNAL
(2022)
Article
Computer Science, Artificial Intelligence
Bosung Kim, Youngjoong Ko, Jungyun Seo
Summary: In neural network models, obtaining high-quality datasets is crucial. To address the problem of class imbalance, a novel regularization method is proposed, which significantly improves the performance on multiple datasets, achieving state-of-the-art results.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Green & Sustainable Science & Technology
Korosh Mahmodi, Mostafa Mostafaei, Esmaeil Mirzaee-Ghaleh
Summary: This study used an electronic nose, artificial neural network, and response surface method to analyze various biodiesel and petroleum diesel blended fuels. The results showed that the artificial neural network method achieved a 100% accuracy in classifying and discriminating pure biodiesel fuels, while the response surface method had an accuracy of 92.4%. The artificial neural network method also demonstrated high accuracy in identifying and classifying different blended fuels, with accuracies ranging from 96.5% to 100%.
SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS
(2022)
Article
Chemistry, Multidisciplinary
Garam Bae, Minji Kim, Wooseok Song, Sung Myung, Sun Sook Lee, Ki-Seok An
Summary: This study systematically investigates the impact of selectivity for a target gas on the prediction accuracy of gas concentration using machine learning. The results show a proportional relationship between selectivity factor and prediction accuracy, suggesting that combining sensors with different selectivity factors can enhance the prediction accuracy.
Article
Computer Science, Artificial Intelligence
Barenya Bikash Hazarika, Deepak Gupta, Parashjyoti Borah
Summary: A new fuzzy twin support vector machine based on affinity and class probability (ACFTSVM) is proposed to enhance the classification performance of the affinity and class probability-based fuzzy support vector machine (ACFSVM). ACFTSVM adds regularization terms to diminish the negative influence of noise, measures the affinity and estimates the class probability of majority class datapoints to decrease the potential of noises. ACFTSVM gives preference to majority class datapoints with higher affinity and class probability, while minimizing the influence of minority class samples with lower affinity and class probability.
KNOWLEDGE AND INFORMATION SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Hong-Jie Xing, Zi-Chuan He
Summary: This study proposes a novel adaptive loss function based LS-OCSVM method to enhance its anti-outlier performance, and it demonstrates better performance on synthetic and benchmark data sets compared to nine related methods.
PATTERN RECOGNITION LETTERS
(2022)
Article
Computer Science, Information Systems
Wenli Song, Lei Zhang, Xinbo Gao
Summary: Zero-shot Learning aims to transfer knowledge from seen image categories to unseen ones by leveraging semantic information. However, existing methods often suffer from biased learned projection functions due to visual distribution differences between seen and unseen objects. In this paper, we propose a Compound Projection Learning (CPL) model that exploits both seen and class-agnostic samples to reduce semantic ambiguity and improve knowledge transfer. We also utilize a similarity network to explore inter-class relationships. Experimental results on ZSL benchmark datasets demonstrate the effectiveness of our approach.
IEEE TRANSACTIONS ON MULTIMEDIA
(2023)
Article
Engineering, Electrical & Electronic
Zhiyuan Wu, Fengchun Tian, James A. Covington, Hantao Li, Siyuan Deng
Summary: Sensor drift is a common issue in electronic noses, leading to decreased long-term performance. Transferring models to different scenarios remains challenging even with drift compensation. To remedy this, a general-purpose calibration approach using different/generic chemicals is needed. This article investigates a method to identify suitable chemicals based on universality, safety, sensibility, and differentiation, and tests it on a homemade electronic nose. Six different chemicals (acetone, alcohol, ethyl acetate, tetrahydrofuran, acetaldehyde, and n-hexane) were selected as the most appropriate for calibration. This research aims to inspire the development of a reasonable chemical selection method for general-purpose electronic noses.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2023)
Article
Computer Science, Artificial Intelligence
Zhenwei He, Lei Zhang, Xinbo Gao, David Zhang
Summary: In this study, a Multi-Adversarial FasterRCNN (MAF) framework is proposed to address the cross-domain object detection task. The framework introduces Hierarchical Domain Feature Alignment (HDFA) and Aggregated Proposal Feature Alignment (APFA) modules to reduce domain disparities and improve detection performance. Furthermore, a Paradigm Teacher MAF (PT-MAF) framework is proposed with knowledge distillation and DualDiscriminator HDFA (D2-HDFA) modules to enhance domain adaptability and alignment.
INTERNATIONAL JOURNAL OF COMPUTER VISION
(2023)
Article
Computer Science, Artificial Intelligence
Shanshan Wang, Lei Zhang, Pichao Wang, MengZhu Wang, Xingyi Zhang
Summary: This paper proposes a BP-Triplet Loss method based on Bayesian learning for unsupervised domain adaptation. The method adjusts the weights of sample pairs, self attends to hard sample pairs, and improves the quality of target pseudo labels through adversarial loss. Experimental results demonstrate the effectiveness of the proposed approach for unsupervised domain adaptation.
PATTERN RECOGNITION
(2023)
Article
Engineering, Electrical & Electronic
Qinghai Lang, Lei Zhang, Wenxu Shi, Weijie Chen, Shiliang Pu
Summary: The Implicit Domain-invariant Faster R-CNN (IDF) is proposed to address the problem of implicit domain-invariant features caused by the multimodal structure of target distribution. By using a non-adversarial domain discriminator, dual attention mechanism, and selective feature perception, IDF outperforms other state-of-the-art domain adaptive object detectors on benchmark datasets.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
(2023)
Article
Engineering, Electrical & Electronic
Zhipu Liu, Lei Zhang, David Zhang
Summary: This paper proposes a Neural Parts Group Search (NPGS) strategy for person re-identification. The optimal parts group is auto-searched using an evolutionary algorithm, allowing the network to exploit local details. A coarse-to-fine parts search space (C2F-PSP) is designed to reduce search complexity without losing parts expressivity. An efficient feature aggregation strategy named hierarchical low-rank bilinear pooling is developed to integrate high-level semantic and low-level fine-grained details. A relational attention module (RAM) is proposed to reduce interference from the background during parts search. Experimental results show that the proposed method outperforms state-of-the-art Re-ID models.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
(2023)
Article
Automation & Control Systems
Junhui Qian, Mengchen Lu, Fengchun Tian, Leilei Zhao, Ailing Zhang
Summary: This article presents a novel sensor array optimization scheme for multisensor electronic nose detection system. A system architecture with multisensor is proposed for medical detection. Two sensor array optimization procedures based on factor analysis and Hilbert-Schmidt independence criterion are derived to improve detection effect and reduce the number of sensors. Experimental results show that the proposed methods can achieve significant system performance compared with existing approaches.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Engineering, Electrical & Electronic
Yuhang Zhou, Fuxiang Huang, Weijie Chen, Shiliang Pu, Lei Zhang
Summary: This paper presents a regularizer for person re-identification models inspired by Adversarial Training. A novel implicit regularizer, named Stochastic Gradient Perturbation (SGP), is proposed to improve the diversity of perturbation, reduce computational cost, and overcome the optimization dilemma between adversarial robustness and accuracy. The experiments demonstrate that SGP achieves powerful performances in both generalization and adversarial robustness for person re-identification.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
(2023)
Article
Geochemistry & Geophysics
Tan Guo, Ruizhi Wang, Fulin Luo, Xiuwen Gong, Lei Zhang, Xinbo Gao
Summary: In this article, a dual-view spectral and global spatial feature fusion network (DSGSF) is proposed for hyperspectral image (HSI) classification. The DSGSF consists of a spatial subnetwork and a spectral subnetwork, which can extract spatial-spectral features with strong discriminating performance.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2023)
Article
Geochemistry & Geophysics
Tan Guo, Long He, Fulin Luo, Xiuwen Gong, Yuanyuan Li, Lei Zhang
Summary: This paper proposes an antinoise hierarchical mutual-incoherence-induced discriminative learning method for hyperspectral image anomaly detection. By applying structural incoherence constraint and first-order statistic constraint, the separability between background and anomalies is improved. A mixed noise model is used to enhance antinoise performance. Experimental results show that the proposed method outperforms other methods.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2023)
Article
Computer Science, Artificial Intelligence
Lei Zhang, Zhipu Liu, Wensheng Zhang, David Zhang
Summary: This paper proposes a feature-based augmentation technique named Style-uncertainty Augmentation (SuA) to increase the diversity of training domains. It also introduces a progressive learning strategy called Self-paced Meta Learning (SpML) to improve the model's generalization ability. Additionally, a distance-graph alignment loss is proposed to leverage domain information and improve the model's generalization.
IEEE TRANSACTIONS ON IMAGE PROCESSING
(2023)
Article
Engineering, Electrical & Electronic
Leilei Zhao, Fengchun Tian, Junhui Qian, Hantao Li, Zhiyuan Wu
Summary: In this article, a feature ensemble-based extreme learning machine framework (FE-ELM) is proposed for GSA data classification. The method performs downsampling on the time series of each sensor and combines the downsampled features to obtain fused feature subsets. Each fused feature subset is trained independently using a base ELM, and the final predictions are obtained by voting the results of all base ELMs. Experimental results show that the proposed FE-ELM surpasses traditional methods and extends the detection limit of the sensor array.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2023)
Article
Computer Science, Information Systems
Fuxiang Huang, Lei Zhang, Yuhang Zhou, Xinbo Gao
Summary: This paper introduces the research topic of image retrieval with text feedback (IRTF) and proposes a regularization approach using gradient augmentation to address the issues of model overfitting and low diversity of training data. Experimental results show that the proposed method achieves better performance on two datasets.
IEEE TRANSACTIONS ON MULTIMEDIA
(2023)
Article
Computer Science, Artificial Intelligence
Lei Zhang, Lingyun Qin, Mingjun Xu, Weijie Chen, Shiliang Pu, Wensheng Zhang
Summary: We propose a meta learning method based on randomized transformations to learn domain invariant object detectors. The method addresses the problem of domain bias in domain generalizable object detection by increasing the diversity of source domains and balancing the gradients among different domains. Experimental results on multiple benchmarks demonstrate that our method achieves state-of-the-art performance.
IEEE TRANSACTIONS ON IMAGE PROCESSING
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Leilei Zhao, Fengchun Tian, Junhui Qian, Ran Liu, Anyan Jiang
Summary: This paper proposes a robust sensor array optimization method based on sparse learning for multi-feature fusion data classification. The method considers the intrinsic group structure among features and eliminates insignificant feature groups through sparse coefficients generated by the model. An efficient alternating iteration algorithm is presented to optimize the objective function. Experimental results demonstrate that the proposed method effectively reduces the number of sensors with improved classification accuracy.
ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING - ICANN 2022, PT IV
(2022)
Article
Chemistry, Analytical
Mengmeng Guo, Na Luo, Yueling Bai, Zhenggang Xue, Qingmin Hu, Jiaqiang Xu
Summary: A porous heterostructure WO3-C/In2O3 was designed and prepared for a miniature H2 sensor, which showed higher response value, lower operating temperature, fast response-recovery speed, and low limit of detection.
SENSORS AND ACTUATORS B-CHEMICAL
(2024)
Article
Chemistry, Analytical
Feng Hu, Hui Hu, Yuting Li, Xiaohui Wang, Xiaowen Shi
Summary: Arsenic contamination in water bodies is a significant health risk. This study developed a chitosan-catechol modified electrode for rapid and accurate detection of trace amounts of arsenic. The modified electrode demonstrated good detection capability and resistance to ionic interference, making it suitable for in situ detection.
SENSORS AND ACTUATORS B-CHEMICAL
(2024)
Article
Chemistry, Analytical
Yantao Zhang, Qian Liu, Tao Tian, Chunhua Xu, Pengli Yang, Lianju Ma, Yi Hou, Hui Zhou, Yongjun Gan
Summary: In this study, a lysosome-targeting buffering fluorogenic probe (Lyso-BFP) was designed and synthesized, demonstrating excellent photostability, pH specificity, and responsiveness to lysosomal acidification in living cells. The performance of Lyso-BFP in pH sensing was attributed to the inhibition of the photo-induced electron transfer process. Lyso-BFP allowed for wash-free imaging and long-term real-time monitoring of lysosome pH changes based on its off-on fluorescence behavior and buffer strategy.
SENSORS AND ACTUATORS B-CHEMICAL
(2024)
Article
Chemistry, Analytical
Wei Cai, Wenbo Sun, Jiayue Wang, Xiaokui Huo, Xudong Cao, Xiangge Tian, Xiaochi Ma, Lei Feng
Summary: In this study, a near-infrared fluorescent probe HCBG was developed for imaging of alpha-GLC. HCBG exhibited excellent selectivity and sensitivity towards alpha-GLC in complex bio-samples, and showed good cell permeability for in situ real-time imaging. Through the high-throughput screening system established by HCBG, a natural alpha-GLC inhibitor was successfully isolated and identified. This study provides a novel fluorescence visualization tool for discovering and exploring the biological functions of diabetes-related gut microbiota, and a high-throughput screening approach for alpha-GLC inhibitor.
SENSORS AND ACTUATORS B-CHEMICAL
(2024)
Article
Chemistry, Analytical
Trey W. Pittman, Xi Zhang, Chamindie Punyadeera, Charles S. Henry
Summary: Heart failure is a growing epidemic and a significant clinical and public health problem. Researchers have developed a portable and affordable diagnostic device for heart failure that can be used at the point-of-care, providing a valid alternative to current diagnostics approaches.
SENSORS AND ACTUATORS B-CHEMICAL
(2024)
Article
Chemistry, Analytical
Anders O. Tjell, Barbara Jud, Roland Schaller-Ammann, Torsten Mayr
Summary: An optical hydrogen peroxide sensor based on catalytic degradation and the detection of produced oxygen is presented. The sensor offers higher resolution and better sensitivity at lower H2O2 concentrations. By removing O2 from the sample solution, a more sensitive O2 sensor can be used for measurement. The sensor has been successfully applied in a flow-through cell to measure H2O2 concentration in different flow rates.
SENSORS AND ACTUATORS B-CHEMICAL
(2024)
Article
Chemistry, Analytical
Seong Jae Kim, Ji-hun Jeong, Gaabhin Ryu, Yoon Sick Eom, Sanha Kim
Summary: Surface-enhanced Raman spectroscopy (SERS) is a high-sensitivity, label-free detection method with various analytical applications. Researchers have developed a hydrophobic SERS substrate based on engineered carbon nanotube arrays (CNT-SERS) and studied the role of structural design at both micro and nanoscales. The substrate demonstrated controlled self-enrichment capability and enhanced sensitivity, with a significant increase in the SERS signal. The study also proposed a theoretical model and a concentration strategy inspired by plants for analyte deposition on microarrays.
SENSORS AND ACTUATORS B-CHEMICAL
(2024)
Article
Chemistry, Analytical
Dan Zhao, Renjun Jiang, Xiaoqiang Liu, Subbiah Alwarappan
Summary: In this study, a novel ternary composite material was constructed by assembling cerium vanadate nanorods on reduced graphene oxide-microcrystalline cellulose nanosheets, and it was used for real-time monitoring of the concentration of superoxide anions in vivo. The ternary composite showed excellent conductivity, large surface area, and abundant active sites, leading to a wider linear range, high sensitivity, low detection limit, and fast response time for superoxide anion detection.
SENSORS AND ACTUATORS B-CHEMICAL
(2024)
Article
Chemistry, Analytical
Tengfei Wang, Liwen Wang, Guang Wu, Dating Tian
Summary: In this study, a covalent organic framework material TaTp-COF with porous and uniform spheres was successfully prepared via hydrothermal reaction, and it was found to significantly enhance the aggregation-induced emission (AIE) of berberine. The unique emission properties of berberine on TaTp-COF were studied and utilized for the sensitive detection of berberine.
SENSORS AND ACTUATORS B-CHEMICAL
(2024)
Article
Chemistry, Analytical
Lin Li, Yilei Ding, Lei Xu, Shuoran Chen, Guoliang Dai, Pengju Han, Lixin Lu, Changqing Ye, Yanlin Song
Summary: In this study, a novel TTI based on a ratiometric fluorescent nanosensor is designed, which has the advantages of high accuracy and low cost. Experimental and theoretical investigations confirm its pH responsiveness and demonstrate its good sensitivity and reliability. By monitoring the total volatile basic nitrogen, this TTI can accurately predict food spoilage and can be adaptively modified for different types of food. The TTI based on this nanosensor enables visual monitoring of food quality.
SENSORS AND ACTUATORS B-CHEMICAL
(2024)
Article
Chemistry, Analytical
Fangju Chen, Xueting Wang, Wei Chen, Chenwen Shao, Yong Qian
Summary: Lung cancer is the second most common malignant tumor worldwide. Drug resistance in lung cancer leads to treatment failure and recurrence in majority of patients. This study developed a fluorescent prodrug that can be activated in cancer cells to release drugs, and its signal can be tracked by imaging. It shows a unique autophagy-driven ferroptosis effect, indicating its potential for targeting drug-resistant cancer cells.
SENSORS AND ACTUATORS B-CHEMICAL
(2024)
Article
Chemistry, Analytical
Weichao Li, Qiming Yuan, Zhangcheng Xia, Xiaoxue Ma, Lifang He, Ling Jin, Xiangfeng Chu, Kui Zhang
Summary: This study developed a high-performance gas sensor for formaldehyde detection by modifying ZnSnO3 with ZnO QDs and SnO2 QDs. The modified sensor showed improved sensing response and lower working temperature. The presence of ZnO QDs formed rich heterojunctions, increased surface area, and provided oxygen deficiency for formaldehyde sensing reaction, thus enhancing the sensor performance. This research provides an alternative method to enhance the sensing properties of MOS by QDs modification.
SENSORS AND ACTUATORS B-CHEMICAL
(2024)
Article
Chemistry, Analytical
Joung-Il Moon, Eun Jung Choi, Younju Joung, Jin-Woo Oh, Sang-Woo Joo, Jaebum Choo
Summary: A novel nanoplasmonic substrate was developed for biomedical applications, which showed strong hot spots for detecting biomarkers at low concentrations. The substrate, called AuNPs@M13, was made by immobilizing 60 nm gold nanoparticles onto the surface of an M13 bacteriophage scaffold. It demonstrated higher sensitivity and lower limit of detection compared to commercially available assays.
SENSORS AND ACTUATORS B-CHEMICAL
(2024)
Article
Chemistry, Analytical
Ning Li, Ya Zhang, Ying Xu, Xiaofang Liu, Jian Chen, Mei Yang, Changjun Hou, Danqun Huo
Summary: The molecular subtype of breast cancer guides treatment and drug selection. Invasive tests can promote cancer cell metastasis, so the development of high-performance, low-cost diagnostic tools for cancer prognosis is crucial. Liquid biopsy techniques enable noninvasive, real-time, dynamic, multicomponent, quantitative, and long-term observations at the cellular, genetic, and molecular levels. A Cu-Zr metal-organic framework (MOF) nanoenzyme with monatomic Cu attachment has been synthesized and proven to have high catalytic performance. The sensor constructed using this nanoenzyme shows potential for accurate classification of breast cancer serum samples.
SENSORS AND ACTUATORS B-CHEMICAL
(2024)
Article
Chemistry, Analytical
Jeongmin Kim, Hyemin Kim, Seunghyun Park, Hyeonaug Hong, Yong Jae Kim, Jiyong Lee, Jaeho Kim, Seung-Woo Cho, Wonhyoung Ryu
Summary: This study presents a method to fabricate independently functioning microneedle (MN) electrodes with narrow intervals for high precision electrochemical sensing. The optimized mixture of photocurable polymer and single-wall carbon nanotubes was used to mold single composite MNs, which were then attached to pre-patterned electrodes. Plasma etching and electropolymerization were performed to enhance the electrochemical activity, and Prussian blue and glucose oxidase were electrodeposited on the MNs for glucose detection. The MN electrodes showed good sensitivity and linearity, and the feasibility of glucose detection was demonstrated in an in vivo mouse study.
SENSORS AND ACTUATORS B-CHEMICAL
(2024)
