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Optica Publishing Group
  • Applied Spectroscopy
  • Vol. 76,
  • Issue 10,
  • pp. 1222-1233
  • (2022)

Green Textile Materials for Surface Enhanced Raman Spectroscopy Identification of Pesticides Using a Raman Handheld Spectrometer for In-Field Detection

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Abstract

Surface enhanced Raman spectroscopy (SERS) has evolved into a powerful analytical method in food and environmental analytical sciences due to its high sensitivity. Pesticide analysis is a major discipline therein. Using sustainable materials has become increasingly important to adhere to Green Chemistry principles. Hence, the green textiles poly-(L-lactic acid) (PLA) and the mixed fabric polyethylene terephthalate polyamide (PET/PA) were investigated for their applicability as solid supports for gold nanoparticles to yield SERS substrates. Gold nanoparticle solutions and green textile supports were prepared after preparation optimization. Particle size, dispersity, and particle distribution over the textiles were characterized by absorption spectroscopy and transmission electron imaging. The performance of the SERS substrates was tested using the three pesticides imidacloprid, paraquat, and thiram and a handheld Raman spectrometer with a laser wavelength of 785 nm. The resulting SERS spectra possessed an intra-substrate variation of 7–8% in terms of the residual standard deviation. The inter-substrate variations amounted to 15% for PET/PA and to 27% for PLA. Substrate background signals were smaller with PLA but more enhanced through PET/PA. The pesticides could be detected at 1 pg on PET/PA and at 3 ng on PLA. Hence, PET/PA woven textile soaked with gold nanoparticle solution provides green SERS substrates and might prove, in combination with fieldable Raman spectrometers, suitable for in-field analytics for pesticide identification.

© 2022 The Author(s)

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Supplementary Material (1)

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Supplement 1       Supplemental Material - Green Textile Materials for Surface Enhanced Raman Spectroscopy Identification of Pesticides Using a Raman Handheld Spectrometer for In-Field Detection

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