Effect of pro‐oxidants on the aerobic biodegradation, disintegration, and physio‐mechanical properties of compostable polymers
Bibliographic record
Abstract
Abstract Biodegradable polymers are gaining momentum to resolve the globally acknowledged plastic waste problem. Understanding, characterizing, and developing new generations of biodegradable plastics is crucial to provide industries with alternative green materials that can fully satisfy biodegradation rates and lifetime specifications. This study evaluates the influence of metal pro‐oxidant additives on the degradation properties of various biodegradable polymer systems. For this purpose, iron (III) stearate (FeSt3) and bismuth oxide (Bi2O3), as oxidant agents, were incorporated into poly(3‐hydroxybutyrate‐co‐3‐hydroxyvalerate) (PHBV), poly(butylene adipate‐co‐terephthalate) (PBAT), cellulose acetate (CA), poly(lactic acid) (PLA), and thermoplastic starch (TPS) bioplastics. The material performances and biodegradability properties due to the additives on the resulting bioplastic formulations were investigated. A mechanism was proposed in which both pro‐oxidant additives can accelerate the thermo‐oxidation processes under composting conditions and cleave the polymer chains into smaller fragments to stimulate the biodegradation rate through microorganisms' activity. The study revealed that both pro‐oxidant additives, FeSt3 and Bi2O3, effectively improved the biodegradation process for all tested polymers except TPS, which already had a very high biodegradation rate. The observed change in the barrier and mechanical properties due to the additives were within tolerable limits of corresponding neat polymers.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".