What question did this study set out to answer?

The aim is to develop an intelligent, self-cleaning SERS platform for on-site detection of antibiotic residues in aquatic environments.

May 16, 2026ACS Sensors0 citations

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An Intelligent Photocatalytic Self-Cleaning SERS Platform with Machine Learning for On-Site Detection of Multiple Antibiotics

ZSZiqi SunJiangnan University YZYuhan ZhangJiangnan University GGGaolin GuoJiangnan University

Puntos clave

The aim is to develop an intelligent, self-cleaning SERS platform for on-site detection of antibiotic residues in aquatic environments.
Validation with real environmental samples including lake water, river water, and shrimp.
Measured recovery rates ranging from 92.49% to 107.10% and precision with RSD below 8.76%.
Evaluated resistance to matrix interference.
Achieved high accuracy in detecting multiple antibiotic residues.
Demonstrated support for practical applications in environmental surveillance and food safety.
Established significant resistance to matrix interference in various aquatic samples.

Resumen

pM. Validation with real environmental samples (lake, river and shrimp) confirmed high accuracy, with recovery rates of 92.49-107.10% and precision (RSD below 8.76%), demonstrating strong resistance to matrix interference. This SERS platform provided a practical and sustainable solution for on-site monitoring of multiple antibiotic residues in aquatic systems, with significant potential for applications in environmental surveillance and food safety.

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Cite This Study

Sun et al. (Wed,) studied this question.

synapsesocial.com/papers/6a0808ffa487c87a6a40b1cf https://doi.org/https://doi.org/10.1021/acssensors.6c00430

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