Burning Rates and Thermal Behavior of Bistetrazole Containing Gun Propellants
Bibliographic record
Abstract
Abstract The influence of two selected bistetrazoles, 5,5′‐bis(1H‐tetrazolyl)‐amine (BTA) and 5,5′‐hydrazinebistetrazole (HBT), on the combustion behavior of a typical triple‐base propellant was investigated. Seven propellant formulations, one reference and six others incorporating 5 %, 15 %, and 25 % of either HBT or BTA compounds, respectively, were mixed and extruded into a cylindrical, no perforations, geometry. The resulting propellants showed high burning rates, up to 93 % higher than the reference formulation at 100 MPa. However, the increase in burning rates came at the cost of higher burning rate dependency on pressure, with a pressure exponent as high as 1.4 for certain formulations. HBT‐containing propellants showed notably lower flame temperature when compared to the reference formulation, with a flame temperature reduction of up to 461 K for the propellant containing 25 % HBT. The thermal behavior of the propellants was also investigated through DSC experiments. The addition of bistetrazoles provided lower decomposition temperatures than the pure nitrogen‐rich materials, indicating that the two compounds probably react readily with the −ONO2 groups present in the nitrocellulose and the plasticizers used in the formulation. The onset temperature of all propellants remained within acceptable ranges despite the observed decrease caused by the addition of the bistetrazole compounds.
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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".