Development of Biodegradable Flame-Retardant Bamboo Charcoal Composites, Part I: Thermal and Elemental Analyses
Bibliographic record
Abstract
In this study, bamboo charcoal (BC) was used as a substitute filler for bamboo powder (BP) in a lignocellulose-plastic composite made from polylactic acid (PLA), with aluminum hypophosphite (AHP) added as a fire retardant. A set of BC/PLA/AHP composites were successfully prepared and tested for flame-retardancy properties. Objectives were to (a) assess compatibility and dispersibility of BC and AHP fillers in PLA matrix, and (b) improve flame-retardant properties of PLA composite. BC reduced flexural properties while co-addition of AHP enhanced bonding between PLA and BC, improving strength and ductility properties. Adding AHP drastically reduced the heat release rate and total heat release of the composites by 72.2% compared with pure PLA. The formation of carbonized surface layers in the BC/PLA/AHP composites effectively improved the fire performance index (FPI) and reduced the fire growth index (FGI). Flame-retardant performance was significantly improved with limiting oxygen index (LOI) of BC/PLA/AHP composite increased to 31 vol%, providing a V-0 rating in UL-94 vertical flame test. Adding AHP promoted earlier initial thermal degradation of the surface of BC/PLA/AHP composites with a carbon residue rate up to 40.3%, providing a protective layer of char. Further raw material and char residue analysis are presented in Part II of this series.
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 | 0.001 | 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".