ArticlePolymers2026
Machine-Learning-Guided Experimental Prioritization of Seaweed-Derived Bioplastic Films Toward an LDPE-like Mechanical Target Using Sparse Literature Data.
Article in Polymers, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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8 authors.
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Abstract
Seaweed polysaccharide films are potential alternatives to petroleum-derived flexible packaging, but literature data are sparse and heterogeneous. This study evaluates whether composition-only machine-learning models can prioritize reported seaweed formulations for experimental follow-up near a nominal low-density polyethylene (LDPE) mechanical target of 15 MPa tensile strength and 300% elongation at break. A published dataset of 115 formulations with 41 compositional predictors was analyzed using a regularized linear baseline (ridge regression), random forest, and a deliberately shallow, regularized XGBoost model. Model performance was estimated using repeated five-fold cross-validation (10 repeats; 50 test-fold evaluations). For tensile strength, mean
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