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Nanomechanics of Drug-target Interactions and Antibacterial Resistance Detection
Published on: October 25, 2013
Hydrophobicity-induced self-assembly of nano-tipped dendritic gold SERS substrates for machine-learning-assisted
Xinyu Chang1, Min Liao2, Shujuan Meng3
1School of Electronic and Information Engineering, Beihang University, Beijing 100191, China; College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen 518118, China.
Abstract:
Antibiotics are widely used in medicine, livestock farming, and agriculture, yet their overuse leads to severe environmental contamination, threatening ecosystems and human health. Surface-Enhanced Raman Scattering (SERS) holds great promise for highly sensitive detection, but its widespread practical implementation is impeded by the lack of substrates that simultaneously possess high reproducibility, ultra-sensitivity, and fabrication economy. Here, we present a hydrophobicity-induced self-assembly strategy to prepare dendritic gold nanoparticles (Au NPs) substrates, overcoming the fundamental trade-off between facile, low-cost preparation and ultrahigh detection sensitivity. By combining diffusion-limited aggregation with an enhanced non-uniform flow field on hydrophobic surfaces, tightly packed dendritic Au NPs are formed. These create ultrahigh-density electromagnetic hot spots with a local field about four times stronger than that of isolated Au NPs pairs. The resulting substrate allows label-free, rapid, and highly sensitive detection of antibiotics, achieving a detection limit of 100 ng L-1 for norfloxacin, 1-2 orders of magnitude better than existing methods. It also discriminates mixtures of norfloxacin and oxytetracycline at 10 μg L-1 via machine-learning-assisted spectral classification, and detecting antibiotic residues down to 1 μg L-1 in river water. Overall, this work offers a simple, cost-effective, and ultrasensitive SERS substrate strategy, providing a practical tool for environmental monitoring, water safety assessment, and biomedical diagnostics.
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