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Machine learning-enabled microfluidic ratiometric fluorescence sensor array based on lanthanide-gold nanoclusters for
Zhaoshuai Shao1, Jian Zhang1, Zhu Jin1
1School of Chemistry and Chemical Engineering, Anhui University of Technology, Ma'anshan, Anhui, 243032, China.
Biosensors & Bioelectronics
|October 16, 2025
Summary
This study presents a novel sensor array using lanthanide-gold nanoclusters and machine learning for detecting multiple antibiotics like oxytetracycline, levofloxacin, and norfloxacin in environmental samples with high sensitivity.
Area of Science:
- Environmental Chemistry
- Nanotechnology
- Analytical Chemistry
Background:
- Antibiotic pollution poses significant environmental and health risks.
- Need for rapid, sensitive, and portable methods for multicomponent antibiotic detection.
Purpose of the Study:
- Develop a microfluidic ratiometric fluorescence sensor array for simultaneous detection of multiple antibiotics.
- Integrate machine learning for enhanced classification and quantification.
Main Methods:
- Utilized lanthanide-gold nanoclusters (AuNCs) as probes targeting specific antibiotics.
- Employed a capillary-driven microfluidic chip for visual RGB fingerprint generation.
- Applied machine learning algorithms for data analysis and classification.
Main Results:
- Achieved ultra-low detection limits for oxytetracycline (OTC), levofloxacin (LEVO), and norfloxacin (NFX).
- Demonstrated high classification accuracy for single, binary, and ternary antibiotic mixtures.
- Established linear detection ranges for each targeted antibiotic.
Conclusions:
- The developed sensor array offers a user-friendly, portable, and scalable solution for multicomponent antibiotic monitoring.
- Integration of microfluidics, fluorescence, and machine learning enables effective detection in complex environmental matrices.
