Cargando…

Sensing Antibiotics in Wastewater Using Surface-Enhanced Raman Scattering

[Image: see text] Rapid and cost-effective detection of antibiotics in wastewater and through wastewater treatment processes is an important first step in developing effective strategies for their removal. Surface-enhanced Raman scattering (SERS) has the potential for label-free, real-time sensing o...

Descripción completa

Detalles Bibliográficos
Autores principales: Huang, Yen-Hsiang, Wei, Hong, Santiago, Peter J., Thrift, William John, Ragan, Regina, Jiang, Sunny
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2023
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10061928/
https://www.ncbi.nlm.nih.gov/pubmed/36934344
http://dx.doi.org/10.1021/acs.est.3c00027
_version_ 1785017393134698496
author Huang, Yen-Hsiang
Wei, Hong
Santiago, Peter J.
Thrift, William John
Ragan, Regina
Jiang, Sunny
author_facet Huang, Yen-Hsiang
Wei, Hong
Santiago, Peter J.
Thrift, William John
Ragan, Regina
Jiang, Sunny
author_sort Huang, Yen-Hsiang
collection PubMed
description [Image: see text] Rapid and cost-effective detection of antibiotics in wastewater and through wastewater treatment processes is an important first step in developing effective strategies for their removal. Surface-enhanced Raman scattering (SERS) has the potential for label-free, real-time sensing of antibiotic contamination in the environment. This study reports the testing of two gold nanostructures as SERS substrates for the label-free detection of quinoline, a small-molecular-weight antibiotic that is commonly found in wastewater. The results showed that the self-assembled SERS substrate was able to quantify quinoline spiked in wastewater with a lower limit of detection (LoD) of 5.01 ppb. The SERStrate (commercially available SERS substrate with gold nanopillars) had a similar sensitivity for quinoline quantification in pure water (LoD of 1.15 ppb) but did not perform well for quinoline quantification in wastewater (LoD of 97.5 ppm) due to interferences from non-target molecules in the wastewater. Models constructed based on machine learning algorithms could improve the separation and identification of quinoline Raman spectra from those of interference molecules to some degree, but the selectivity of SERS intensification was more critical to achieve the identification and quantification of the target analyte. The results of this study are a proof-of-concept for SERS applications in label-free sensing of environmental contaminants. Further research is warranted to transform the concept into a practical technology for environmental monitoring.
format Online
Article
Text
id pubmed-10061928
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher American Chemical Society
record_format MEDLINE/PubMed
spelling pubmed-100619282023-03-31 Sensing Antibiotics in Wastewater Using Surface-Enhanced Raman Scattering Huang, Yen-Hsiang Wei, Hong Santiago, Peter J. Thrift, William John Ragan, Regina Jiang, Sunny Environ Sci Technol [Image: see text] Rapid and cost-effective detection of antibiotics in wastewater and through wastewater treatment processes is an important first step in developing effective strategies for their removal. Surface-enhanced Raman scattering (SERS) has the potential for label-free, real-time sensing of antibiotic contamination in the environment. This study reports the testing of two gold nanostructures as SERS substrates for the label-free detection of quinoline, a small-molecular-weight antibiotic that is commonly found in wastewater. The results showed that the self-assembled SERS substrate was able to quantify quinoline spiked in wastewater with a lower limit of detection (LoD) of 5.01 ppb. The SERStrate (commercially available SERS substrate with gold nanopillars) had a similar sensitivity for quinoline quantification in pure water (LoD of 1.15 ppb) but did not perform well for quinoline quantification in wastewater (LoD of 97.5 ppm) due to interferences from non-target molecules in the wastewater. Models constructed based on machine learning algorithms could improve the separation and identification of quinoline Raman spectra from those of interference molecules to some degree, but the selectivity of SERS intensification was more critical to achieve the identification and quantification of the target analyte. The results of this study are a proof-of-concept for SERS applications in label-free sensing of environmental contaminants. Further research is warranted to transform the concept into a practical technology for environmental monitoring. American Chemical Society 2023-03-19 /pmc/articles/PMC10061928/ /pubmed/36934344 http://dx.doi.org/10.1021/acs.est.3c00027 Text en © 2023 The Authors. Published by American Chemical Society https://creativecommons.org/licenses/by/4.0/Permits the broadest form of re-use including for commercial purposes, provided that author attribution and integrity are maintained (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Huang, Yen-Hsiang
Wei, Hong
Santiago, Peter J.
Thrift, William John
Ragan, Regina
Jiang, Sunny
Sensing Antibiotics in Wastewater Using Surface-Enhanced Raman Scattering
title Sensing Antibiotics in Wastewater Using Surface-Enhanced Raman Scattering
title_full Sensing Antibiotics in Wastewater Using Surface-Enhanced Raman Scattering
title_fullStr Sensing Antibiotics in Wastewater Using Surface-Enhanced Raman Scattering
title_full_unstemmed Sensing Antibiotics in Wastewater Using Surface-Enhanced Raman Scattering
title_short Sensing Antibiotics in Wastewater Using Surface-Enhanced Raman Scattering
title_sort sensing antibiotics in wastewater using surface-enhanced raman scattering
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10061928/
https://www.ncbi.nlm.nih.gov/pubmed/36934344
http://dx.doi.org/10.1021/acs.est.3c00027
work_keys_str_mv AT huangyenhsiang sensingantibioticsinwastewaterusingsurfaceenhancedramanscattering
AT weihong sensingantibioticsinwastewaterusingsurfaceenhancedramanscattering
AT santiagopeterj sensingantibioticsinwastewaterusingsurfaceenhancedramanscattering
AT thriftwilliamjohn sensingantibioticsinwastewaterusingsurfaceenhancedramanscattering
AT raganregina sensingantibioticsinwastewaterusingsurfaceenhancedramanscattering
AT jiangsunny sensingantibioticsinwastewaterusingsurfaceenhancedramanscattering