Akademik Veri Yönetim Sistemi
19TH Asian Chemical Congress, İstanbul, Türkiye, 9 - 14 Temmuz 2023, ss.260
Bacteria infect millions of people every year, and thousands of people die from these infections. With the increasing antibiotic resistance of bacteria, traditional treatment methods are insufficient and the threat is growing day by day. Therefore, the best way to fight harmful bacteria is to fight them outside before they infect the human body. Here, we present nanostructured coatings that can combine anti-biofouling, bactericidal and bacteria detection-identification properties in a single platform. In the study, end-functional polyethylene glycol brushes were grafted onto glass surfaces, and then hydrophobic silica nanoparticles were deposited on the surface, leading to transparent superhydrophobic coatings. This coating was further modified by physical vapor deposition of silver, which had a broad-spectrum antibacterial activity. After the deposition of 10 nm film of silver, the coating has a static contact angle of 169° and sliding angle of 3°. This superhydrophobicity has given the platform the ability to prevent initial attack, adhesion and contamination by bacteria. Moreover, the platform exhibited over 99.9% bactericidal activity against Gram-positive S. aureus and Gram-negative E. coli bacteria through silver nanostructures. In addition, the platform exhibited high surface-enhanced Raman scattering (SERS) activity with the plasmonic surface resonance of silver, enabling label-free and sensitive detection of bacteria at low cell counts (1000 CFU/mL). Finally, the platform was coupled with machine learning algorithms for bacterial species identification and 5 different species of bacteria were automatically identified from SERS spectra with over 90% accuracy using support vector machines. With all these features, the platform can be a widely effective solution to prevent material and moral losses caused by bacterial contamination.
Keywords: Anti-biofouling, Bactericidal, SERS, Bacteria Detection, Machine Learning, Bacteria Identification