Examination of a Biobased Carbon Nucleating Agent on Poly(lactic acid) Crystallization
Bibliographic record
Abstract
This article assesses the performance of a biobased carbon as a nucleator using common techniques to stimulate poly(lactic acid) crystallization and enhance the thermal stability of PLA during injection molding. The combination of a biodegradable plasticizer, poly(ethylene glycol) (PEG), along with biobased carbon-rich pyrolyzed biomass char residue and an industrially available microcrystalline talc, were tested for nucleating agent capabilities at additions of 10 wt%. Differential scanning calorimetry (DSC) data demonstrated that the inclusion of the plasticizer could increase the PLA crystalline content with further improvements when nucleating agent was present. With a higher mold temperature, the PLA crystallinity surpassed 40% for the multicomponent formulations. The thermomechanical properties including heat deflection temperature exhibited performance above 100 °C, while the coefficient of thermal expansion was lowered. Optical analysis of the crystal structure showed increased crystallization rate with plasticizer and nucleating agents. The mechanical properties and morphology characteristics are also presented.
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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".