Improvement of the Thermal Decomposition of Ammonium Perchlorate and Combustion of Aluminum Powder by Dual Core–Shell Ammonium Perchlorate-Based Composites Based on Self-Assembly Coating
Bibliographic record
Abstract
Ammonium perchlorate (AP), which is the most commonly used oxidant in composite propellants, plays a vital role in the combustion performance of propellants. In this work, by coating the AP surface with a self-assembled coating layer of gallic acid (GA) and transition metal ions (Cu 2+ or Fe 3+ ) and then connecting it with a 1 H,1 H,2 H,2 H -perfluorodecyltrichlorosilane (PDTS) coating layer, the AP@GA-M@PDTS (M = Cu 2+ or Fe 3+ ) composite was successfully prepared. Molecular dynamics simulation calculations show that GA and AP have a good binding ability. At the same time, the binding ability of PDTS and GA is also much higher than that of PDTS and AP. In addition, the peak temperature of high-temperature decomposition and activation energy of AP@GA-M@PDTS (M = Cu 2+ or Fe 3+ ) are lower than those of AP, and the heat release during the thermal decomposition process is much higher than AP. Compared with pure AP, the surface coating properties of AP@GA-M@PDTS (M = Cu 2+ or Fe 3+ ) have also changed significantly. AP@GA-M@PDTS (M = Cu 2+ or Fe 3+ ) can float on water; its static contact angle with the water surface is much higher than that of pure AP, and it absorbs almost no moisture after being placed in humid air for 30 days. Combustion experiments show that the burning speed and burning intensity of AP@GA-M@PDTS (M = Cu 2+ or Fe 3+ )/Al are significantly higher than those of AP/Al, and AP@GA-M@PDTS (M = Cu 2+ or Fe 3+ )/Al has a stronger emission spectrum. Finally, the catalytic mechanism of AP@GA-M@PDTS (M = Cu 2+ or Fe 3+ ) to improve the combustion of aluminum powder is discussed. In summary, it is a very promising solid propellant additive.
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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".