Comparative Environmental Life Cycle Assessment on Corn Starch Plasticization and Co-Plasticization Processes
Bibliographic record
Abstract
Starch has overtaken the bioplastic market in developing thermoplastic starch-based blends and composite systems owing to its biodegradability and sustainability. Thermoplastic starch (TPS) development is mostly a two-stage process involving plasticizing starch and blending plasticized starch with a polymer. Most of the research focuses on improving the properties of the blend system through different methodologies, including various plasticizers and co-plasticizers. However, limited studies have analyzed the environmental effects of plasticizers or co-plasticizers and their processing. Thus, in this research, the environmental impact of starch plasticization processes performed by co-plasticization (glycerol–urea, glycerol–citric acid, and glycerol–succinic anhydride) and by conventional glycerol-based plasticization is compared through life cycle assessment (LCA). The results showed that glycerol–citric acid- and glycerol–succinic anhydride-based co-plasticization had a comparable environmental impact to traditional glycerol-based plasticization. In contrast, the glycerol–urea-based co-plasticization process exhibited the highest effect on the environment. Furthermore, to reduce the environmental impact, a sensitivity analysis of the plasticization processes was conducted by changing the energy aspect of the processes through quantitative and qualitative approaches. The qualitative approach significantly reduced major impact categories such as global warming, carcinogens, ecotoxicity, and fossil fuel depletion.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".