Intelligent analysis of carbendazim in agricultural products based on a ZSHPC/MWCNT/SPE portable nanosensor combined with machine learning methods

Wang, Xu; Li, He; Xu, Lulu; Liu, Zhongshou; Xiong, Yao; Zhou, Weiqi; Yao, Hang; Wen, Yang; Geng, Xin; Wu, Ruimei

doi:10.1039/d2ay01779b

Cited by 7 publications

(4 citation statements)

References 44 publications

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“…Aer incubation with CPF solutions for 15 minutes, the electrode underwent another SWV test in the same test solution, and the peak current was recorded as I 1 . The inhibition rate of peak current was calculated by eqn (1). The schematic diagram of pesticide detection is depicted in Scheme 1B.…”

Section: Electrochemical Measurementsmentioning

confidence: 99%

“…CPF is an organophosphorus pesticide that has been widely used for pest control in vegetables, fruits, grains and other crops since 1965. 1 CPF is already used in nearly 100 countries, and is likely to be even more widely used in the next 30 years due to food production needs. 2 However, the continued widespread use and abuse of CPF over the years has resulted in it being the most common pesticide entering the food chain and affecting human health.…”

Section: Introductionmentioning

confidence: 99%

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A new plant-esterase inhibition based electrochemical sensor with signal amplification by MoS₂@N-CDs for chlorpyrifos detection

Chen,

Ji,

Wang

et al. 2024

RSC Adv.

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Section: Electrochemical Measurementsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A new plant-esterase inhibition based electrochemical sensor with signal amplification by MoS₂@N-CDs for chlorpyrifos detection

Chen,

Ji,

Wang

et al. 2024

RSC Adv.

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“…Wang et al [165] utilised a similar system to detect the fungicide carbendazim (CBZ) in rice and tea using carbon nanocomposite electrodes functionalized with ZnS nanoparticles. SVM, a least-square support vector machine (LS-SVM), neural networks and a linear regression model were trained to predict the CBZ residues.…”

Section: Electrochemical Sensorsmentioning

confidence: 99%

Machine Learning Techniques for Improving Nanosensors in Agroenvironmental Applications

Arellano Vidal,

Govan

2024

Agronomy

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Nanotechnology, nanosensors in particular, has increasingly drawn researchers’ attention in recent years since it has been shown to be a powerful tool for several fields like mining, robotics, medicine and agriculture amongst others. Challenges ahead, such as food availability, climate change and sustainability, have promoted such attention and pushed forward the use of nanosensors in agroindustry and environmental applications. However, issues with noise and confounding signals make the use of these tools a non-trivial technical challenge. Great advances in artificial intelligence, and more particularly machine learning, have provided new tools that have allowed researchers to improve the quality and functionality of nanosensor systems. This short review presents the latest work in the analysis of data from nanosensors using machine learning for agroenvironmental applications. It consists of an introduction to the topics of nanosensors and machine learning and the application of machine learning to the field of nanosensors. The rest of the paper consists of examples of the application of machine learning techniques to the utilisation of electrochemical, luminescent, SERS and colourimetric nanosensor classes. The final section consists of a short discussion and conclusion concerning the relevance of the material discussed in the review to the future of the agroenvironmental sector.

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“…Machine learning (ML) is becoming a critical tool in various fields of nanomaterials research, such as nanoindentation [ 317 , 318 , 319 , 320 , 321 , 322 , 323 , 324 , 325 , 326 , 327 , 328 ], nanorobotics [ 329 , 330 , 331 , 332 , 333 , 334 ], and nanosensor [ 335 , 336 , 337 , 338 , 339 , 340 ] development. Its ability to analyze and interpret complex patterns from large datasets is particularly beneficial in advancing areas like nanostructured materials analysis, nanoscale manufacturing processes, and the development of nanotechnology applications in medicine and environmental monitoring [ 327 , 341 , 342 , 343 , 344 , 345 , 346 , 347 , 348 , 349 , 350 , 351 , 352 , 353 , 354 , 355 , 356 , 357 , 358 , 359 , 360 , 361 , 362 , 363 , 364 , …”

Section: Introductionmentioning

confidence: 99%

A 30-Year Review on Nanocomposites: Comprehensive Bibliometric Insights into Microstructural, Electrical, and Mechanical Properties Assisted by Artificial Intelligence

Gomes Souza,

Bhansali,

Pal

et al. 2024

Materials

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From 1990 to 2024, this study presents a groundbreaking bibliometric and sentiment analysis of nanocomposite literature, distinguishing itself from existing reviews through its unique computational methodology. Developed by our research group, this novel approach systematically investigates the evolution of nanocomposites, focusing on microstructural characterization, electrical properties, and mechanical behaviors. By deploying advanced Boolean search strategies within the Scopus database, we achieve a meticulous extraction and in-depth exploration of thematic content, a methodological advancement in the field. Our analysis uniquely identifies critical trends and insights concerning nanocomposite microstructure, electrical attributes, and mechanical performance. The paper goes beyond traditional textual analytics and bibliometric evaluation, offering new interpretations of data and highlighting significant collaborative efforts and influential studies within the nanocomposite domain. Our findings uncover the evolution of research language, thematic shifts, and global contributions, providing a distinct and comprehensive view of the dynamic evolution of nanocomposite research. A critical component of this study is the “State-of-the-Art and Gaps Extracted from Results and Discussions” section, which delves into the latest advancements in nanocomposite research. This section details various nanocomposite types and their properties and introduces novel interpretations of their applications, especially in nanocomposite films. By tracing historical progress and identifying emerging trends, this analysis emphasizes the significance of collaboration and influential studies in molding the field. Moreover, the “Literature Review Guided by Artificial Intelligence” section showcases an innovative AI-guided approach to nanocomposite research, a first in this domain. Focusing on articles from 2023, selected based on citation frequency, this method offers a new perspective on the interplay between nanocomposites and their electrical properties. It highlights the composition, structure, and functionality of various systems, integrating recent findings for a comprehensive overview of current knowledge. The sentiment analysis, with an average score of 0.638771, reflects a positive trend in academic discourse and an increasing recognition of the potential of nanocomposites. Our bibliometric analysis, another methodological novelty, maps the intellectual domain, emphasizing pivotal research themes and the influence of crosslinking time on nanocomposite attributes. While acknowledging its limitations, this study exemplifies the indispensable role of our innovative computational tools in synthesizing and understanding the extensive body of nanocomposite literature. This work not only elucidates prevailing trends but also contributes a unique perspective and novel insights, enhancing our understanding of the nanocomposite research field.

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Intelligent analysis of carbendazim in agricultural products based on a ZSHPC/MWCNT/SPE portable nanosensor combined with machine learning methods

Abstract: A nano-ZnS-decorated hierarchically porous carbon (ZSHPC) was mixed with MWCNTs to obtain ZSHPC/MWCNT nanocomposites.

Cited by 7 publications

References 44 publications

A new plant-esterase inhibition based electrochemical sensor with signal amplification by MoS₂@N-CDs for chlorpyrifos detection

A new plant-esterase inhibition based electrochemical sensor with signal amplification by MoS₂@N-CDs for chlorpyrifos detection

Machine Learning Techniques for Improving Nanosensors in Agroenvironmental Applications

A 30-Year Review on Nanocomposites: Comprehensive Bibliometric Insights into Microstructural, Electrical, and Mechanical Properties Assisted by Artificial Intelligence

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Intelligent analysis of carbendazim in agricultural products based on a ZSHPC/MWCNT/SPE portable nanosensor combined with machine learning methods

Abstract: A nano-ZnS-decorated hierarchically porous carbon (ZSHPC) was mixed with MWCNTs to obtain ZSHPC/MWCNT nanocomposites.

Cited by 7 publications

References 44 publications

A new plant-esterase inhibition based electrochemical sensor with signal amplification by MoS2@N-CDs for chlorpyrifos detection

A new plant-esterase inhibition based electrochemical sensor with signal amplification by MoS2@N-CDs for chlorpyrifos detection

Machine Learning Techniques for Improving Nanosensors in Agroenvironmental Applications

A 30-Year Review on Nanocomposites: Comprehensive Bibliometric Insights into Microstructural, Electrical, and Mechanical Properties Assisted by Artificial Intelligence

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Product

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A new plant-esterase inhibition based electrochemical sensor with signal amplification by MoS₂@N-CDs for chlorpyrifos detection

A new plant-esterase inhibition based electrochemical sensor with signal amplification by MoS₂@N-CDs for chlorpyrifos detection