Year: 2025
Type: article
Abstract: This work introduces, for the first time, an innovative bio-based flame retardant (FR) system forbiocomposites, integrating experimental insights and machine learning (ML) to optimize both compositionand performance. By employing a computationally guided, cost-efficient experimentationstrategy, we systematically combine design of experiments for space exploration, ML-driven propertyprediction, and optimization methods to rapidly identify high-performance formulations. Crucially,this approach demonstrates how data-driven techniques can be seamlessly incorporated into conventionalexperimental material design, ensuring proper sampling of the design space and leveraging thecollected data to generate new predictions and optimize the properties of these sustainable materials.As a result, mechanical strength is significantly enhanced and fire safety improved, minimizing relianceon resource-intensive trial-and-error processes. The optimal formulation achieved an 18.4% increasein tensile strength (TS) and a 53.1% reduction in the peak heat release rate (pHRR) compared tothe neat polymer. Bayesian optimization further validated individual optimal solutions, delivering upto a 22.3% improvement in TS and a 73.7% reduction in pHRR. Overall, this research establishesa digitally integrated workflow that accelerates the development of sustainable, high-performancebiocomposites and bio-based flame retardants, providing eco-friendly alternatives to conventionalfire-safe polymeric materials.
Source: Journal of Materials Chemistry A
DOI: 10.1039/d5ta02511g
Institutions: IMDEA Materials, Universidad Carlos III de Madrid
Language: English
Subfield: Polymers and Plastics
Field: Materials Science
Domain: Physical Sciences
Open Access status: gold
Awards: G3480869486, G5080475149, G7084143925
OpenAlex ID: W4410324252