The fast identification of bacterial pathogens and profiling of antibiotic resistance could greatly facilitate the precise treatment strategy of infectious diseases. Traditional methods for detecting pathogens in bacteremia patients are time-consuming. As a result, physicians may make treatment decisions based on an incomplete diagnosis, potentially increasing the patient’s risk of death. To overcome these drawbacks, we intend to create, characterize, and test novel hybrid nanostructures of metal/two-dimensional nanomaterials for surface-enhanced Raman spectroscopy, which will be used to detect bacterial species in clinical samples. First, as a substrate material for the specific detection of the biosamples, we will use mechanical exfoliation of high-quality 2D materials onto metallic surfaces (i)under ambient and (ii) ultra-high vacuum (UHV) conditions and surface lattice resonance demonstrating regular structures of silver nanoparticles covered with graphene. The higher SERS enhancement factors, the oxidation protection of the metal surface, and the protection of molecules from photo-induced damage are all advantages of the SERS hybrid substrate platform. In addition, advanced machine learning methods, including deep learning algorithms, will be recruited for the analysis of the multi-dimensional SERS spectral data. Taken together, this study will broaden the application scope of the SERS technique, which will not only open new avenues for the development of novel SERS substrates but also significantly facilitate the targeted identification of pathogenic bacteria in clinical samples.
Project funding:
Research Council of Lithuania (RCL), Projects of Postdoctoral fellowships funded by the state budget of the Republic of Lithuania
Period of project implementation: 2024-04-02 - 2026-04-01
Project coordinator: Kaunas University of Technology
