Biopolymers are increasingly recognised as a sustainable alternative for packaging and coatings, but their complex and variable properties make formulation and commercialisation slow and costly. This project will advance an Explainable AI (XAI) formulation platform from TRL 4 to TRL 6 to accelerate the discovery of high-performance biopolymer coatings for food and healthcare applications.
The platform will combine machine learning with experimental and open-source datasets to predict key performance characteristics, including tensile strength, barrier properties, antimicrobial efficacy, and biodegradability. AI-driven screening will reduce laboratory experimentation, while XAI will provide transparent, interpretable predictions suitable for scientists, industry users, and regulatory stakeholders.
Over 24 months, predictions will be validated through pilot-scale manufacturing and industry testing, creating the evidence required for technology transfer, investment, and commercial adoption. A dual-IP strategy will protect the underlying algorithmic architecture as trade secrets while patenting novel material formulations.
The project will bridge the gap between laboratory research and industrial deployment, targeting pilot agreements with Tier-1 partners and establishing the basis for an investable spin-out delivering sustainable biopolymer solutions for packaging and coatings, with particular potential to reduce food waste.
