文献类型: 外文期刊
作者: Xiang, Ling 1 ; Zhao, Chunjiang 1 ; Wang, Jihua 1 ;
作者机构: 1.Beijing Res Ctr Agrifood Testing & Farmland Monit, Beijing 100097, Peoples R China
2.Beijing Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China
关键词: Nanomaterials;Electrochemical;Biosensors;Pesticide Detection
期刊名称:SENSOR LETTERS ( 影响因子:0.558; 五年影响因子:0.58 )
ISSN:
年卷期:
页码:
收录情况: SCI
摘要: This paper reviews recent advances in developing novel nanomaterials-based electrochemical sensors and biosensors for rapid pesticide detection in food. These nanosensors have emerged as a highly effective method for pesticide detection (e.g., organophosphate pesticides, carbamate pesticides) with superior advantages over the existing classical techniques, such as gas chromatography or high-performance liquid chromatography. The use of unique nanomaterials, including carbon nanotubes, gold nanoparticles, quantum dots and other nanomaterials, has greatly enhanced the performance of pesticide sensors and biosensors in sensitivity, selectivity, stability and on-site analysis. The construction, characterization, and application of these nanosensors are described in detail. Future trends and challenges of these nanosensors are also discussed.
- 相关文献
作者其他论文 更多>>
-
Recognition of wheat rusts in a field environment based on improved DenseNet
作者:Chang, Shenglong;Cheng, Jinpeng;Fan, Zehua;Ma, Xinming;Li, Yong;Zhao, Chunjiang;Chang, Shenglong;Yang, Guijun;Cheng, Jinpeng;Fan, Zehua;Yang, Xiaodong;Zhao, Chunjiang
关键词:Plant disease; Wheat rust; Image processing; Deep learning; Computer vision (CV); DenseNet
-
GCVC: Graph Convolution Vector Distribution Calibration for Fish Group Activity Recognition
作者:Zhao, Zhenxi;Zhao, Chunjiang;Zhao, Zhenxi;Yang, Xinting;Zhou, Chao;Zhao, Chunjiang;Zhao, Zhenxi;Yang, Xinting;Zhou, Chao;Zhao, Chunjiang;Zhao, Zhenxi;Yang, Xinting;Zhou, Chao;Zhao, Chunjiang;Liu, Jintao
关键词:Fish; Feature extraction; Activity recognition; Calibration; Adhesives; Training; Convolution; Graph convolution vector calibration; fish group activity; activity feature vector calibration; fish activity dataset
-
Adaptive precision cutting method for rootstock grafting of melons: modeling, analysis, and validation
作者:Chen, Shan;Zhao, Chunjiang;Chen, Shan;Jiang, Kai;Zheng, Wengang;Jia, Dongdong;Zhao, Chunjiang;Jiang, Kai;Zheng, Wengang;Jia, Dongdong;Zhao, Chunjiang
关键词:Melon; Grafting robot; Adaptive cutting; Rootstock pith cavity; Machine vision
-
Long-range infrared absorption spectroscopy and fast mass spectrometry for rapid online measurements of volatile organic compounds from black tea fermentation
作者:Yang, Chongshan;Li, Guanglin;Zhao, Chunjiang;Fu, Xinglan;Yang, Chongshan;Jiao, Leizi;Wen, Xuelin;Lin, Peng;Duan, Dandan;Zhao, Chunjiang;Dong, Daming;Yang, Chongshan;Jiao, Leizi;Wen, Xuelin;Lin, Peng;Duan, Dandan;Dong, Daming;Dong, Chunwang
关键词:Black tea fermentation; Volatile organic compounds; Proton transfer reaction mass spectrometry; Fourier transform infrared spectroscopy; Principal component analysis; Extreme learning machine
-
Navigation line extraction algorithm for corn spraying robot based on YOLOv8s-CornNet
作者:Guo, Peiliang;Diao, Zhihua;Ma, Shushuai;He, Zhendong;Zhao, Suna;Zhao, Chunjiang;Li, Jiangbo;Zhang, Ruirui;Yang, Ranbing;Zhang, Baohua
关键词:agricultural robotics; computer vision; deep learning; navigation line extraction; network lightweight
-
An ultra-lightweight method for individual identification of cow-back pattern images in an open image set
作者:Wang, Rong;Gao, Ronghua;Li, Qifeng;Zhao, Chunjiang;Ding, Luyu;Yu, Ligen;Ma, Weihong;Wang, Rong;Zhao, Chunjiang;Gao, Ronghua;Li, Qifeng;Zhao, Chunjiang;Ding, Luyu;Yu, Ligen;Ma, Weihong;Ru, Lin
关键词:Cow-back pattern; Cow recognition; LightCowsNet; Open image set; Deep learning
-
Unveiling the hidden impact: How biodegradable microplastics influence CO 2 and CH 4 emissions and Volatile Organic Compounds (VOCs) profiles in soil ecosystems
作者:Wang, Yihao;Zhao, Chunjiang;Lu, Anxiang;Dong, Daming;Gong, Wenwen;Wang, Yihao
关键词:Biodegradable microplastics; Paddy and upland soils; Greenhouse gases; Volatile Organic Compounds; Optical gas sensor