Integrated Data Modeling and Biological Evaluation of PEGylated Konjac Gum-Rosin Pentaerythritol Polymeric Nanocomposites for Enhanced Antimicrobial Performance

Abstract

The increasing prevalence of antibiotic-resistant pathogens necessitates the development of novel antimicrobial agents. Herein, PEGylated konjac gum-supported rosin pentaerythritol nanocomposites (KG/PEG/RE PNCs) were synthesized using an environmentally friendly sonochemical method, aiming to explore their potential antibacterial and antifungal properties against a range of pathogens, including Candida albicans, Escherichia coli, Pseudomonas aeruginosa, Aspergillus brasiliensis, and Staphylococcus aureus. An elaborate investigation into the rheological properties of these PNCs highlighted the dependence of viscosity on synthesis parameters such as RE concentration, sonication time, and KG/RE blend ratio with the Higiro model validated as a suitable mathematical model for defining the intricate relationship between experimental and resulting viscosity of PNCs. The integration of machine learning (ML), particularly polynomial regression, enabled the modeling of the complex dynamics influencing PNC viscosity, thus advancing comprehension of PNCs behavior in relation to the synthesis parameters. The modeling facilitated precise formulation to predict PNC viscosity with high accuracy, as confirmed by a mean squared error (MSE) of 3.81 and an R2 of 0.993. Moreover, the PNCs demonstrated broad-spectrum antimicrobial activity, reaching an inhibition plateau during the first week, confirming its efficacy as a versatile antibacterial and antifungal agent. Combining advanced data modeling techniques with biological assessments, this integrated approach represents a step forward in understanding and optimizing polymeric nanostructures.