Nowadays, nanomaterials are a real alternative for controlling
antibiotic-resistant bacteria. Several nanoparticles have shown good
performance in inducing bacterial death due to a photoassisted process.
This study employs statistical analyses and machine learning (ML)
models to investigate the effect of doping ZnO nanoparticles with
cerium ions on their antibacterial activity in a dark environment.
Incorporating cerium ions into the ZnO matrix was systematically analyzed
in terms of structural, morphological, and optical parameters. The
incorporation of cerium ions did not modify the crystal structure.
These results were correlated with their qualitative and quantitative
antibacterial activity against Escherichia coli, Staphylococcus aureus, and Pseudomonas aeruginosa to establish the impact of
doping. No significant differences in their antibacterial activity
were observed. A maximum of 95% bacterial growth inhibition was observed.
ML tools were utilized to model bacterial survival under different
conditions. The support vector machine (SVM) model yielded the highest
prediction error. In contrast, the extremely random tree model produced
an error of only 1.8%, making it an excellent computational tool for
decision-making. Using this framework, we conducted attribute importance
analysis based on the model, identifying a small subset of parameters
as being crucial for generating a precise model. The findings provide
light on the most critical characteristics of cerium-doped ZnO nanoparticles’
antibacterial activity.