Fast synthesis of MoO<sub>3-x</sub> and its catalytic effect on the thermal decomposition of ammonium perchlorate based molecular perovskite (DAP-4)
Bibliographic record
Abstract
In this work, it is shown that MoO3-x has a positive effect on the thermal decomposition of ammonium perchlorate based molecular perovskite (H2DABCO)[NH4(ClO4)3] (DAP-4). MoO3-x was prepared by heat treatment, and the morphology, structure, and thermal decomposition performance were characterized. The morphology and structure characterization results showed that MoO3-x was an irregular layered structure material and the Mo element was mainly in the +6 chemical valence state, with a small amount of Mo5+. Thermal analysis results showed that the thermal decomposition peak temperature of DAP-4 was effectively reduced from 394.4 °C to 353.7 °C, 321.4 °C, and 312.5 °C in the presence of 1%, 5%, and 10% MoO3-x, respectively. It is particularly worth noting that the maximum heat release rate of the DAP-4/10% MoO3-x mixture was increased by 4.9 times compared with pure DAP-4. Through the two classic thermal decomposition kinetic methods, Kissinger and Starink, the reliable kinetic parameters of DAP-4/MoO3-x were obtained. The increase of the reaction rate constant k indicated that the maximum thermal decomposition reaction rate of DAP-4 was effectively improved. This work provided a feasible technology for using MoO3-x as an effective catalyst to improve the thermal performance of DAP-4.
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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".