Statistical design of sustainable composites from poly(lactic acid) and grape pomace
Bibliographic record
Abstract
Abstract Biocomposites from poly(lactic acid) (PLA) and grape pomace (GP) were created via injection molding to examine the effects of GP in a PLA matrix. To optimize the mechanical performance the biocomposites were compatibilized with maleic anhydride grafted PLA (MA‐g‐PLA). The objective of this work was to create a model that could accurately predict the mechanical properties of GP/PLA biocomposites. A region of feasibility for the biocomposites was determined using a statistical design of experiments. Linear regression was used to model the mechanical performance and predicted results with an error of 10% for both tensile and flexural strength and 16% for impact strength. The model was verified with a biocomposite of PLA/GP/MA‐g‐PLA with a ratio of 62/36/2. This biocomposite had a tensile strength, flexural modulus, and impact strength of 25.8 MPa, 40.0 MPa, and 18.4 J/m, respectively. It was found that a linear model can accurately predict the mechanical properties of PLA/GP/MA‐g‐PLA biocomposites.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".