Dual-function antimicrobial and SERS-active colloidal silver nanoparticle platform for the detection of opportunistic pathogens assisted by machine learning
| dc.contributor.author | Sahin, Furkan | |
| dc.date.accessioned | 2026-01-31T15:08:12Z | |
| dc.date.available | 2026-01-31T15:08:12Z | |
| dc.date.issued | 2026 | |
| dc.department | İstanbul Beykent Üniversitesi | |
| dc.description.abstract | The rapid control of opportunistic pathogens-through both their detection and elimination-is crucial to mitigate health risks, especially for immunocompromised individuals. In this study, silver nanoparticles (AgNPs) were synthesized via a simple and environmentally friendly approach, using ascorbic acid reduction under medical steam sterilizer conditions, and evaluated as a dual-function colloidal platform that integrates antimicrobial activity and surface-enhanced Raman scattering (SERS)-based pathogen identification. Structural characterization using scanning electron microscopy, zeta potential, UV-Vis spectroscopy, and X-ray diffraction confirmed uniform morphology, high colloidal stability, and crystallinity. The AgNP substrates provided robust and reproducible SERS signals, achieving an analytical enhancement factor of approximately 106 at concentrations down to 1 nM, with sample-to-sample variability below 5%. The antimicrobial assays demonstrated rapid biocidal activity, with > 99% (R > 2) of Escherichia coli, Staphylococcus aureus, and Candida albicans eliminated within 1 h, and significant bactericidal efficacy (R > 6) maintained over 48 h. Importantly, the controlled antimicrobial interaction facilitated the release of intracellular components, yielding more consistent and distinctive spectral profiles that enhanced species-level discrimination. Principal component analysis (PCA) revealed clear separation among pathogen-specific spectral clusters, while a linear support vector machine (SVM) classifier achieved 95.7% accuracy, confirming strong discriminative capability. Overall, this study serves as a proof of concept for a dual-function colloidal AgNP platform, synthesized via an accessible autoclave-assisted green approach, that combines antimicrobial action with SERS-based pathogen identification in a simple and reproducible format. | |
| dc.description.sponsorship | Health Institutes of Turkiye (TUSEB) [35683] | |
| dc.description.sponsorship | The author would like to thank Prof. Mustafa Serdar Onses (Erciyes University) for providing access to Raman spectroscopy facilities and for his valuable support. Some schematic elements used in Fig. 1 were created with the assistance of BioRender.com under a trial license. The author would like to express their gratitude to BioRender.com for providing this platform. The author thanks the Health Institutes of Turkiye (TUSEB) for providing microbiological testing resources through project support under project number 35683. | |
| dc.identifier.doi | 10.1007/s11051-025-06532-7 | |
| dc.identifier.issn | 1388-0764 | |
| dc.identifier.issn | 1572-896X | |
| dc.identifier.issue | 1 | |
| dc.identifier.scopus | 2-s2.0-105026839848 | |
| dc.identifier.scopusquality | Q2 | |
| dc.identifier.uri | https://doi.org./10.1007/s11051-025-06532-7 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12662/10619 | |
| dc.identifier.volume | 28 | |
| dc.identifier.wos | WOS:001655010500001 | |
| dc.identifier.wosquality | Q2 | |
| dc.indekslendigikaynak | Web of Science | |
| dc.indekslendigikaynak | Scopus | |
| dc.language.iso | en | |
| dc.publisher | Springer | |
| dc.relation.ispartof | Journal of Nanoparticle Research | |
| dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | |
| dc.rights | info:eu-repo/semantics/closedAccess | |
| dc.snmz | KA_WoS_20260128 | |
| dc.subject | SERS | |
| dc.subject | Antimicrobial | |
| dc.subject | Silver nanoparticles | |
| dc.subject | Machine learning | |
| dc.subject | Opportunistic pathogens | |
| dc.subject | Nanobiomedicine | |
| dc.title | Dual-function antimicrobial and SERS-active colloidal silver nanoparticle platform for the detection of opportunistic pathogens assisted by machine learning | |
| dc.type | Article |












