Effect of Ammonium Polyphosphate to Aluminum Hydroxide Mass Ratio on the Properties of Wood-Flour/Polypropylene Composites
Bibliographic record
Abstract
Two halogen-free inorganic flame retardants, ammonium polyphosphate (APP) and aluminum hydroxide (ATH) were added to wood-flour/polypropylene composites (WPCs) at different APP to ATH mass ratios (APP/ATH ratios), with a constant total loading of 30 wt % (30% by mass). Water soaking tests indicated a low hygroscopicity and/or solubility of ATH as compared to APP. Mechanical property tests showed that the flexural properties were not significantly affected by the APP/ATH ratio, while the impact strength appeared to increase with the increasing ATH/APP ratio. Cone calorimetry indicated that APP appeared to be more effective than ATH in reducing the peak of heat release rate (PHRR). However, when compared to the neat WPCs, total smoke release decreased with the addition of ATH but increased with the addition of APP. Noticeably, WPCs containing the combination of 20 wt % APP and 10 wt % ATH (WPC/APP-20/ATH-10) showed the lowest PHRR and total heat release in all of the formulations. WPCs combustion residues were analyzed by scanning electron microscopy, laser Raman spectroscopy, and Fourier transform infrared spectroscopy (FTIR). Thermogravimetric analysis coupled with FTIR spectroscopy was used to identify the organic volatiles that were produced during the thermal decomposition of WPCs. WPC/APP-20/ATH-10 showed the most compact carbonaceous residue with the highest degree of graphitization.
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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) | 0.001 | 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".