Strategy for improving the energy output efficiency of TKX-50: introduction of nitroamine explosives
Bibliographic record
Abstract
TKX-50 has attracted the attention of many researchers because of its low sensitivity and high energy characteristics. However, the experimental results show that there is a big gap between the energy level of TKX-50 and the theoretical value. Introducing nitroamine explosives RDX or HMX to stimulate the energy release of TKX-50 is of great significance for improving the energy release efficiency of TKX-50 and expanding its application in the field of energetic materials. In this paper, the properties of different TKX-50/RDX and TKX-50/HMX samples were investigated by thermal analysis and ignition combustion experiments. The results show that RDX has a more obvious catalytic effect on TKX-50. Adding 50% RDX reduces the peak thermal decomposition temperature of TKX-50 from 241.7°C to 218.7°C. At the same time, the flame propagation, maximum combustion pressure, and the pressurization rate of different samples during combustion were analyzed, and the reaction mechanism between RDX (or HMX) with TKX-50 was obtained. Because of the different catalytic effects of RDX and HMX on TKX-50, a strategy was proposed to construct the core-shell structure of TKX-50 to improve the reaction characteristics and energy release efficiency of TKX-50.
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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".