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 MoO 3-x has a positive effect on the thermal decomposition of ammonium perchlorate based molecular perovskite (H 2 DABCO)[NH 4 (ClO 4 ) 3 ] (DAP-4). MoO 3-x was prepared by heat treatment, and the morphology, structure, and thermal decomposition performance were characterized. The morphology and structure characterization results showed that MoO 3-x was an irregular layered structure material and the Mo element was mainly in the +6 chemical valence state, with a small amount of Mo 5+ . 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% MoO 3-x , respectively. It is particularly worth noting that the maximum heat release rate of the DAP-4/10% MoO 3-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/MoO 3-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 MoO 3-x as an effective catalyst to improve the thermal performance of DAP-4.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".