Fabrication and characterization of melt-blended polylactide-chitin composites and their foams
Bibliographic record
Abstract
This study details the fabrication and foaming of melt-blended polylactide (PLA) and chitin composites. The chitin used for compounding was as-received, as chitin nanowhiskers and as chitin nanowhiskers with a compatibilizing agent. The chitin nanowhiskers were produced by an acid-hydrolysis technique and their morphology was examined with transmission electron microscopy. The composite morphology was characterized with scanning electron microscopy and was related to the observed thermal, rheological, and mechanical behaviors of the composites. Chitin was found to decrease the thermal stability of the composites. Addition of chitin was also found to reduce the viscosity of the composites, which is believed to be because of the hydrolysis of PLA during melt blending of chitin in suspension. The stiffness of the composites was found to increase with increasing chitin content while the strength was found to decrease. Porous PLA—chitin composites were produced by a two-step batch-foaming technique, and the expansion behavior was correlated with the visco-elastic observations. The statistical significance of chitin type and composition dependence on the mechanical properties and foam morphologies were evaluated.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".