Publications by authors named "O Guselnikova"
Article Synopsis
- Antibiotic resistance, particularly with beta-lactam antibiotics, poses a significant health threat, highlighting the need for quick and accurate detection methods to improve patient care and combat resistance spread.
- This study presents a novel approach using surface-enhanced Raman spectroscopy (SERS) combined with machine learning (ML) to effectively identify genetic markers associated with antibiotic resistance in bacterial plasmids.
- The SERS-ML technique demonstrated effective detection of resistant plasmids even in challenging biological samples, offering a simpler and faster alternative to current monitoring methods for antibiotic-resistant bacteria.
Human activity is the cause of the continuous and gradual grooving of environmental contaminants, where some released toxic and dangerous compounds cannot be degraded under natural conditions, resulting in a serious safety issue. Among them are the widely occurring water-soluble perfluoroalkyl and polyfluoroalkyl substances (PFAS), sometimes called "forever chemicals" because of the impossibility of their natural degradation. Hence, a reliable, expressive, and simple method should be developed to monitor and eliminate the risks associated with these compounds.
View Article and Find Full Text PDFDeveloping a reliable method for constructing mesoporous metal-organic frameworks (MOFs) with single-crystalline forms remains a challenging task despite numerous efforts. This study presents a solvent-mediated assembly method for fabricating zeolitic imidazolate framework (ZIF) single-crystal nanoparticles with a well-defined micro-mesoporous structure using polystyrene--poly(ethylene oxide) diblock copolymer micelles as a soft-template. The precise control of particle sizes, ranging from 85 to 1200 nm, is achieved by regulating nucleation and crystal growth rates while maintaining consistent pore diameters in mesoporous nanoparticles and a rhombohedral dodecahedron morphology.
View Article and Find Full Text PDFArticle Synopsis
- * This study introduces a new sensing platform that uses macroporous-mesoporous silver substrates with self-assembled polymeric micelles for simultaneous separation and analysis of multiple MP types through surface-enhanced Raman spectroscopy (SERS).
- * A neural network algorithm, SpecATNet, is developed to analyze SERS data, effectively identifying six common types of MPs while handling complex mixtures and interferences, paving the way for field monitoring of microplastics.