ArticlePolymers2025
Mechanical and Thermal Behavior of Hemp-Reinforced Starch/Agar Biocomposites: Insights from Finite Element Simulation and Machine Learning Models.
Article in Polymers, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Article
- Recent Advances and Future Directions in Thermal, Electrical, and Mechanical Properties of Polymer Composites.Polymers · 2025Article
- Review
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The increasing effects of plastic pollution have led to the study of eco-friendly and biodegradable alternatives. The present study concerns the production and characterization of biocomposite films from starch, agar, and alkaline/peroxide-treated hemp fiber powder. In total, nine films were fabricated with variable ratios of starch-agar (0, 0.5, and 0.75) and hemp fiber (0, 15, and 30 wt.%). The physical, mechanical, and thermal properties of these films were evaluated. Tensile tests demonstrated that adding 30% hemp fiber increased Young's modulus, while 15% fiber decreased tensile strength. In the SAH group, adding 1.5 g of agar significantly improved tensile strength and Young's modulus, especially in the SAH30 sample. Finite element simulations of tensile tests showed remarkable agreement with experimental data. Machine learning models (SVM and GP) were used to predict tensile strength, with the SVM model using the RBF kernel showing the highest accuracy (R
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Registered trials
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