Journal of Polymer & Composites

Artificial intelligence (AI) has proven an efficient method to optimize the design and manufacture of polymer nanocomposites, allowing the proper prediction of the behavior of the materials and the results of the processing.

This study presents an AI-optimized nano-silica reinforced polymer composite phase change material (PCM) for predictive solar-thermal energy storage networks.

The use of bio-based polymer composites in additive manufacturing is becoming more and more attractive, but their mechanical properties are very sensitive to the loading of reinforcement, the orientation of the raster and defects of the printing process.

This study developed multifunctional polymer composites based on a PLA/PBAT (Polylactic Acid/ Poly(butylene adipate-co-terephthalate)) thermoplastic matrix reinforced with 20 wt.% corn husk fibre (CHF) and functionalized with conductive carbon black (CB) for smart consumer-electronics casings.

Thermoset polymer nanocomposites exhibit properties that are highly sensitive to molecular scale formulation decisions, yet the vast design space remains largely unexplored because of the high cost of experimental characterisation and fully atomistic simulation.

Catheter-associated infections (CAIs) remain a major clinical challenge. Microbial adhesion and subsequent biofilm formation on catheter surfaces significantly reduce the effectiveness of antimicrobial therapy, resulting in prolonged hospitalization, increased healthcare costs, and higher patient morbidity.