Experimental and machine learning-based investigation of ZnO-incorporated polyurethane/polyethylene glycol nanocomposite films for potential antibacterial urinary catheter film applications

Catheter-related urinary tract infections (CAUTIs) remain a critical healthcare-associated complication, motivating the development of antibacterial polymeric materials for catheter applications. In this study, zinc oxide (ZnO) nanoparticles prepared under two calcination conditions (designated as nominal <50 nm and nominal <100 nm size groups) were incorporated into polyurethane/polyethylene glycol (PU/PEG) films by solvent casting at loadings of 5, 10, 20, and 30 wt%. The materials were characterized using Fourier-transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), water contact-angle measurements, and disk-diffusion assays against Escherichia coli and Staphylococcus aureus. The antibacterial results demonstrated concentration-dependent inhibition, reaching maximum inhibition zones of 17 mm against E. coli and 16 mm against S. aureus at 30 wt% nominal <50 nm ZnO. To rigorously evaluate structure–property–function relationships and prevent model overfitting on a compact dataset, an exploratory Random Forest regression model was integrated using Leave-One-Out Cross-Validation (LOOCV). The cross-validated model showed good fit within the studied dataset. Quantitative feature importance ranking confirmed that ZnO concentration is the main governing factor of antibacterial efficacy (90.6%), followed by surface contact angle (8.3%), whereas nominal particle size category (0.4%) and bacterial species (0.6%) contributed minimally to overall variance. The results demonstrate the potential of cross-validated machine learning for exploratory feature screening in biomaterial design and indicate that ZnO-incorporated PU/PEG films warrant further investigation for potential urinary catheter applications. Direct coating adhesion, mechanical durability, cytocompatibility, release kinetics, biofilm inhibition, and performance on actual catheter substrates require future evaluation.

Authors

Institutions

Publication Details

Journal
Journal of Biomaterials Science Polymer Edition
Published
2026-09-11
DOI
https://doi.org/10.1080/09205063.2026.2728707
Primary Topic
Urinary Tract Infections Management
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
article

Experimental and machine learning-based investigation of ZnO-incorporated polyurethane/polyethylene glycol nanocomposite films for potential antibacterial urinary catheter film applications

Haleeful Jud, PhD Haya Aijaz, Nisar Ahmad Wani
Journal of Biomaterials Science Polymer Edition
Urinary Tract Infections Management
article

Experimental and machine learning-based investigation of ZnO-incorporated polyurethane/polyethylene glycol nanocomposite films for potential antibacterial urinary catheter film applications

Haleeful Jud, PhD Haya Aijaz, Nisar Ahmad Wani
article en

Abstract

Catheter-related urinary tract infections (CAUTIs) remain a critical healthcare-associated complication, motivating the development of antibacterial polymeric materials for catheter applications. In this study, zinc oxide (ZnO) nanoparticles prepared under two calcination conditions (designated as nominal <50 nm and nominal <100 nm size groups) were incorporated into polyurethane/polyethylene glycol (PU/PEG) films by solvent casting at loadings of 5, 10, 20, and 30 wt%. The materials were characterized using Fourier-transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), water contact-angle measurements, and disk-diffusion assays against Escherichia coli and Staphylococcus aureus. The antibacterial results demonstrated concentration-dependent inhibition, reaching maximum inhibition zones of 17 mm against E. coli and 16 mm against S. aureus at 30 wt% nominal <50 nm ZnO. To rigorously evaluate structure–property–function relationships and prevent model overfitting on a compact dataset, an exploratory Random Forest regression model was integrated using Leave-One-Out Cross-Validation (LOOCV). The cross-validated model showed good fit within the studied dataset. Quantitative feature importance ranking confirmed that ZnO concentration is the main governing factor of antibacterial efficacy (90.6%), followed by surface contact angle (8.3%), whereas nominal particle size category (0.4%) and bacterial species (0.6%) contributed minimally to overall variance. The results demonstrate the potential of cross-validated machine learning for exploratory feature screening in biomaterial design and indicate that ZnO-incorporated PU/PEG films warrant further investigation for potential urinary catheter applications. Direct coating adhesion, mechanical durability, cytocompatibility, release kinetics, biofilm inhibition, and performance on actual catheter substrates require future evaluation.

Journal of Biomaterials Science Polymer Edition
Swami Vivekanand Subharti University (IN), Government College of Science (PK)
Openalex Percentile: Top 10%
Urinary Tract Infections Management
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.