Enhancing energy release of aluminized propellants and explosives through fluorinated binder
Bibliographic record
Abstract
Abstract The use of fluorinated binders can enhance the combustion properties of Al in energetic materials. The underlying mechanism is under investigation and a rational strategy in terms of application has yet to be fully developed. In this study, we have investigated the effect of using a fluorine‐modified hydroxy‐terminated polyether (HTPE) binder to cast aluminized propellants and explosives. We have focused on the combustion behavior and energy release characteristics of Al particles with and without the fluorinated binder during propellant combustion and explosive detonation. The propellants combustion process was recorded using a high‐speed camera and an infrared thermometer. The heat of detonation, detonation velocity and thermal stability of the explosives were investigated using a constant temperature calorimeter, an electrometric method and a small‐scale thermal cook‐off test, respectively. The fluorine‐modified HTPE propellant has exhibited a higher flame temperature and greater energy release efficiency than the HTPE propellant. Moreover, the fluorine‐modified HTPE propellant is characterized by smaller particle agglomerates, leading to a reduction in the mass percentage of agglomerates from 73 wt% to 42 wt%. The detonation heat of fluorine‐modified HTPE explosive increases from 75.2 % to 81 %, but the detonation velocity decreased from 7745 m/s to 7622 m/s. In addition, the fluorine‐modified HTPE binder maintained the thermal stability of explosives due to a milder decomposition before thermal runaway.
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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".