Machine Learning-Assisted Pesticide Detection on a Flexible Surface-Enhanced Raman Scattering Substrate Prepared by Silver Nanoparticles

Sahin F., Celik N., Camdal A., ŞAKİR M., CEYLAN A., Ruzi M., ...More

ACS Applied Nano Materials, vol.5, no.9, pp.13112-13122, 2022 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 5 Issue: 9
  • Publication Date: 2022
  • Doi Number: 10.1021/acsanm.2c02897
  • Journal Name: ACS Applied Nano Materials
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex
  • Page Numbers: pp.13112-13122
  • Keywords: silver nanoparticles, Cedrus libani, eco-friendly fabrication, SERS platform, pesticides, machine learning, BIOSYNTHESIZED SILVER, GREEN SYNTHESIS, SERS SUBSTRATE, PAPER, NANOSTRUCTURES, GOLD, AG, FABRICATION, EXTRACTION, RESIDUES
  • Kayseri University Affiliated: No


© 2022 The Authors. Published by American Chemical Society.Access to clean water is a pressing challenge affecting millions of lives and the aquatic body of the Earth. Sensitive detection of pollutants such as pesticides is particularly important to address this challenge. This study reports eco-friendly preparation of the surface-enhanced Raman scattering (SERS) substrate for machine learning-assisted detection of pesticides in water. The proposed SERS platform was prepared on a copy paper by reducing a silver salt using the extract of a natural plant, Cedrus libani. The fabricated SERS platform was characterized in detail using scanning electron microscopy, energy-dispersive X-ray spectroscopy, X-ray diffraction, and X-ray photoelectron spectroscopy. The high-density formation of silver nanoparticles with an average diameter of 41 nm on the surface of the paper enabled detection of analytes with a nanomolar level sensitivity. This SERS capability was used to collect Raman signals of four different pesticides in water: myclobutanil, phosmet, thiram, and abamectin. Raman spectra of the pesticides are highly complex, challenging accurate determination of the pesticide type. To overcome this challenge and distinguish pesticides, machine learning (ML) approach was used. The ML-mediated detection of harmful pesticides on a versatile, green, and inexpensive SERS platform appears to be promising for environmental applications.