Artificial Intelligence

AI-driven reinforcement learning enables polymer design optimized for performance and manufacturability.

Advancements in biotechnology highlight how engineered microbial catalysts can recycle plastic waste in vivo.

A newly developed, open-access tool helps plastic recyclers pinpoint appropriate market applications.

AI-aided polyurethane simulation reduces modeling time from days to seconds. Digital material twins optimize tool design and predict foaming behavior.

Artificial Intelligence (AI) may help bridge the gap between scientific research and policy in the plastics industry.

Researchers are fine-tuning computer vision systems to help identify and sort plastic waste on construction sites.

Plastic manufacturing data analytics shifts quality control upstream. Real-time monitoring and AI improve OEE and identify root causes before defects occur.

Digitalization bridges the knowledge gap in the plastics industry. AI tools, smart displays, and chatbots capture expert data for future generations.

AI-assisted polymer design screens millions of candidates to identify chemically recyclable packaging polymers that still meet barrier and thermal targets.

Machine learning (ML) enables rapid design of antimicrobial peptide (AMP)-mimetic polymers to treat bacterial infections.