Colloids and Surfaces B: Biointerfaces, vol.264, 2026 (SCI-Expanded, Scopus)
Magnetic nanoparticle based concentration of microorganisms is an effective strategy for improving detection efficiency in low biomass biological samples. In this study, the microorganism concentration performance of three magnetic metal oxide nanoparticles, iron oxide magnetic nanoparticles (IOMNPs), cobalt oxide magnetic nanoparticles (COMNPs), and nickel oxide magnetic nanoparticles (NOMNPs), was comparatively evaluated using experimental, statistical, and artificial neural network (ANN) based approaches. Three clinically relevant microorganisms, Escherichia coli, Staphylococcus aureus, and Candida albicans, were examined under varying experimental conditions, including nanoparticle concentration, microorganism concentration, and incubation time. A total of 360 experiments were conducted to assess parameter effects. Classical statistical analyses were applied to identify significant differences among nanoparticle systems, while ANN modeling was used to capture non-linear relationships between experimental variables. The results demonstrated that microorganism concentration was the dominant factor influencing recovery efficiency across all nanoparticle types, whereas the effects of nanoparticle concentration and incubation time were nanoparticle and microorganism dependent. Comparative analyses revealed small but statistically differences among metal oxide nanoparticles, with IOMNPs generally exhibiting higher recovery performance than NOMNPs. ANN modeling showed strong predictive accuracy and consistently identified microorganism concentration, microorganism type, and nanoparticle type as the most influential parameters. Clinical applicability was validated using 280 cerebrospinal fluid samples, where magnetic nanoparticle based concentration outperformed conventional centrifugation without producing false positive results. This study represents the first systematic comparison of multiple magnetic metal oxide nanoparticles under identical experimental and clinical conditions.