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Detection and Identification of Pesticides in Fruits Coupling to an Au-Au Nanorod Array SERS Substrate and RF-1D-CNN Model Analysis

In this research, a method was developed for fabricating Au-Au nanorod array substrates through the deposition of large-area Au nanostructures on an Au nanorod array using a galvanic cell reaction. The incorporation of a granular structure enhanced both the number and intensity of surface-enhanced R...

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Published in:Nanomaterials (Basel, Switzerland) Switzerland), 2024-04, Vol.14 (8), p.717
Main Authors: Sha, Pengxing, Zhu, Chushu, Wang, Tianran, Dong, Peitao, Wu, Xuezhong
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description In this research, a method was developed for fabricating Au-Au nanorod array substrates through the deposition of large-area Au nanostructures on an Au nanorod array using a galvanic cell reaction. The incorporation of a granular structure enhanced both the number and intensity of surface-enhanced Raman scattering (SERS) hot spots on the substrate, thereby elevating the SERS performance beyond that of substrates composed solely of an Au nanorod. Calculations using the finite difference time domain method confirmed the generation of a strong electromagnetic field around the nanoparticles. Motivated by the electromotive force, Au ions in the chloroauric acid solution were reduced to form nanostructures on the nanorod array. The size and distribution density of these granular nanostructures could be modulated by varying the reaction time and the concentration of chloroauric acid. The resulting Au-Au nanorod array substrate exhibited an active, uniform, and reproducible SERS effect. With 1,2-bis(4-pyridyl)ethylene as the probe molecule, the detection sensitivity of the Au-Au nanorod array substrate was enhanced to 10 M, improving by five orders of magnitude over the substrate consisting only of an Au nanorod array. For a practical application, this substrate was utilized for the detection of pesticides, including thiram, thiabendazole, carbendazim, and phosmet, within the concentration range of 10 to 5 × 10 M. An analytical model combining a random forest and a one-dimensional convolutional neural network, referring to the important variable-one-dimensional convolutional neural network model, was developed for the precise identification of thiram. This approach demonstrated significant potential for biochemical sensing and rapid on-site identification.
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subjects 1D-CNN model analysis
Algorithms
Analysis
Arrays
Artificial neural networks
Au–Au nanorod array
Biocompatibility
Carbendazim
Chloroauric acid
Electric potential
Electrolytic cells
Electromagnetic fields
Electromotive forces
Finite difference time domain method
Food safety
Fungicides
galvanic cell reaction
Gold
Identification and classification
Mathematical models
Methods
Nanoparticles
Nanorods
Nanostructure
Neural networks
pesticide
Pesticides
Properties
Raman spectra
Raman spectroscopy
Reproducibility
Simulation
Spectrum analysis
Structure
Substrates
surface-enhanced Raman scattering
Thiabendazole
Thiram
title Detection and Identification of Pesticides in Fruits Coupling to an Au-Au Nanorod Array SERS Substrate and RF-1D-CNN Model Analysis
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