Micro‐explosions caused by the reaction of nano‐sized aluminium and polyvinylidene fluoride to promote the micron‐sized aluminium combustion and inhibit its agglomeration
Bibliographic record
Abstract
Abstract Micron‐aluminium (μAl) powder is difficult to ignition and prone to agglomeration during combustion, leading to the reduced combustion efficiency. To solve the issues, Al powders with different particle sizes were modified by polyvinylidene fluoride (PVDF) and studied using thermogravimetry–differential scanning calorimetry (TG‐DSC), laser ignition experiments, and advanced characterization techniques. These methods revealed that violent micro‐explosions caused by the interaction of PVDF and nano‐Al (nAl) can effectively promote the combustion of μAl and inhibit its agglomeration. The mixing of nAl PVDF and μAl PVDF accelerates the oxidation reaction, increases heat release, and improves combustion performance, particularly for the nAl PVDF /μAl PVDF (1:4) sample, which exhibits the largest flame area, the highest flame expansion speed and flame temperature, and the shortest ignition delay and combustion time. The synergistic effect of PVDF and nAl enhances the fragmentation of μAl during combustion, resulting in smaller condensed combustion products. These results provide insights from both thermal analysis and combustion experiments, laying a foundation for improving the ignition and combustion efficiency of μAl.
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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".