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author:

Liu, J. (Liu, J..) [1] | Meng, X. (Meng, X..) [2] | Hu, C. (Hu, C..) [3] | Wang, S. (Wang, S..) [4] | Tang, J. (Tang, J..) [5] | Luo, K. (Luo, K..) [6] | Lin, Z. (Lin, Z..) [7]

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Scopus

Abstract:

Sulfur-containing metallic salts (SCMs) are widespread environmental pollutants and present varied toxicity risks, posing potential threats to human health. Therefore, developing effective analytical methods to detect trace levels of SCMs is crucial for environmental safety and public health protection. In this work, three iron-based metal covalent organic frameworks (Fe-COFs) nanozymes with peroxidase (POD)-like activity were synthesized through the post-synthetic metallization strategy. Leveraging their prominent POD-like activity that catalyzed the oxidation of 3,3′,5,5′-tetramethylbenzidine (TMB) to produce oxidized TMB generating two characteristic absorption peaks, we developed a six-channel nanozymes sensor array for the identification and detection of six SCMs. Subsequently, machine learning techniques including principal component analysis (PCA), decision trees (DT), random forests (RF), artificial neural networks (ANN), and hierarchical cluster analysis (HCA) were integrated to visualize array responses, enabling accurate identification and prediction of SCMs in complex matrices, with a lower limit of distinction as low as 100 nM. Furthermore, the practical application ability of the sensor array was validated through the successful discrimination of binary/ternary SCMs mixtures, unknown samples and actual samples. This study presents an innovative machine learning-integrated multi-signal nanozymes sensing platform, offering a novel avenue for the construction metal covalent organic frameworks (MCOFs)-based nanozymes sensor arrays for the intelligent identification and detection of SCMs. © 2025 Elsevier B.V.

Keyword:

Intelligent recognition Machine learning Metal covalent organic framework Nanozymes sensor array Sulfur-containing metallic salts

Community:

  • [ 1 ] [Liu J.]Ministry of Education Key Laboratory of Analytical Science for Food Safety and Biology, Fujian Provincial Key Laboratory of Analysis and Detection Technology for Food Safety, College of Chemistry, Fuzhou University, Fujian, Fuzhou, 350108, China
  • [ 2 ] [Meng X.]Ministry of Education Key Laboratory of Analytical Science for Food Safety and Biology, Fujian Provincial Key Laboratory of Analysis and Detection Technology for Food Safety, College of Chemistry, Fuzhou University, Fujian, Fuzhou, 350108, China
  • [ 3 ] [Hu C.]Ministry of Education Key Laboratory of Analytical Science for Food Safety and Biology, Fujian Provincial Key Laboratory of Analysis and Detection Technology for Food Safety, College of Chemistry, Fuzhou University, Fujian, Fuzhou, 350108, China
  • [ 4 ] [Wang S.]Ministry of Education Key Laboratory of Analytical Science for Food Safety and Biology, Fujian Provincial Key Laboratory of Analysis and Detection Technology for Food Safety, College of Chemistry, Fuzhou University, Fujian, Fuzhou, 350108, China
  • [ 5 ] [Tang J.]Ministry of Education Key Laboratory of Analytical Science for Food Safety and Biology, Fujian Provincial Key Laboratory of Analysis and Detection Technology for Food Safety, College of Chemistry, Fuzhou University, Fujian, Fuzhou, 350108, China
  • [ 6 ] [Luo K.]Ministry of Education Key Laboratory of Analytical Science for Food Safety and Biology, Fujian Provincial Key Laboratory of Analysis and Detection Technology for Food Safety, College of Chemistry, Fuzhou University, Fujian, Fuzhou, 350108, China
  • [ 7 ] [Lin Z.]Ministry of Education Key Laboratory of Analytical Science for Food Safety and Biology, Fujian Provincial Key Laboratory of Analysis and Detection Technology for Food Safety, College of Chemistry, Fuzhou University, Fujian, Fuzhou, 350108, China

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Source :

Sensors and Actuators B: Chemical

ISSN: 0925-4005

Year: 2025

Volume: 444

6 . 3 9 3

JCR@2018

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SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 2

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