Enhancement of Flame Retardancy and Mechanical Properties of Polylactic Acid with a Biodegradable Fire-Retardant Filler System Based on Bamboo Charcoal
Bibliographic record
Abstract
A cooperative flame-retardant system based on natural intumescent-grafted bamboo charcoal (BC) and chitosan (CS) was developed for polylactic acid (PLA) with improved flame retardancy and minimal decline in strength properties. Chitosan (CS) as an adhesion promoter improved the interfacial compatibility between graft-modified bamboo charcoal (BC-m) and PLA leading to enhanced tensile properties by 11.11% and 8.42%, respectively for tensile strength and modulus. At 3 wt.% CS and 30 wt.% BC-m, the crystallinity of the composite increased to 38.92%, or 43 times that of pure PLA (0.9%). CS promotes the reorganization of the internal crystal structure. Thermogravimetric analysis showed significantly improved material retention of PLA composites in nitrogen and air atmosphere. Residue rate for 5 wt.% CS and 30 wt.% BC-m was 29.42% which is 55.1% higher than the theoretical value of 18.97%. Flammability tests (limiting oxygen index-LOI and UL-94) indicated significantly improved flame retardancy and evidence of cooperation between CS and BC-m, with calculated cooperative effectiveness index(Ce) >1. From CONE tests, the peak heat release rate (pHRR) and total heat release (THR) were reduced by 26.9% and 30.5%, respectively, for 3% CS + 20% BC-m in PLA compared with adding 20% BC-m alone. Analysis of carbon residue morphology, chemical elements and structure suggest CS and BC-m form a more stable char containing pyrophosphate. This char provides heat insulation to inhibit complete polymer pyrolysis, resulting in improved flame retardancy of PLA composites. Optimal mix may be recommended at 20% BC-m + 3% CS to balance compatibility, composite strength properties and flame retardance.
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) | 0.000 | 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".