Image‐Derived Structure–Morphology–Activity Relationships in Cu–Zn Mixed Metal Oxide/Poly(Glycidyl Methacrylate) Nanocomposites

ABSTRACT Cu–Zn mixed metal oxide/poly(glycidyl methacrylate) (PGMA) nanocomposites were synthesized by alkaline precipitation followed by copper‐mediated controlled radical polymerization and evaluated against Escherichia coli , Staphylococcus aureus , and Bacillus subtilis . Fourier transform infrared spectroscopy, X‐ray diffraction, scanning electron microscopy (SEM), particle size distribution analysis, and agar well diffusion assays supported oxide formation, crystallinity, hierarchical aggregation, and concentration‐dependent antibacterial activity. To strengthen structure–morphology–activity interpretation when raw instrumental data were unavailable, quantitative descriptors were extracted from reported figures. Digitized spectroscopic and diffraction data constrained phase identity and crystallite size, while contrast‐limited adaptive histogram equalization‐assisted SEM processing and morphology‐based segmentation quantified domain size, number density, and projected segmented‐area fraction. These descriptors characterize mesoscale morphology rather than elemental composition. Particle size data further supported aggregation across multiple length scales. An Effective Accessible Surface Index (EASI) was introduced as a relative geometric measure of apparent mesoscale accessibility. Exploratory comparison of EASI and delivered‐dose parameters with inhibition zones indicated an association between dispersion, apparent morphological accessibility, and antibacterial response, particularly for E. coli . This uncertainty‐aware framework demonstrates how image‐derived metrics can complement conventional characterization while maintaining a clear distinction between morphological segmentation, chemical mapping, and causal mechanistic interpretation.