Gradient Denitration Strategy Eliminates Phthalates Associated Potential Hazards During Gun Propellant Production and Application
Bibliographic record
Abstract
Abstract Phthalates, which often have to be used as deterrents during gun propellants production for realizing progressive burning, are widely believed to be harmful to human and the environment. Meanwhile, phthalates also generate much smoke during propellant combustion, thus, lowering firing accuracy and exposing positions, which may bring risk. To avoid phthalates usage during propellants production, this work, for the first time, reported the employment of “gradient denitration” strategy to prepare nitrocellulose‐based gun propellant without the addition of any phthalate deterrents. The successful preparation of gradiently denitrated gun propellant (GDGP) was supported by FT‐IR, Raman spectroscopy and FESEM equipped with energy‐dispersive X‐ray spectroscopy (EDS), as evidenced by the gradiently increased content of nitrogen and nitrate group from the surface to the core of GDGP. Such a denitration process, without any hazardous phthalates deterrents addition, could also realize the good progressive burning performance of the gun propellant, as confirmed by closed bomb test and ballistic gun test. Meanwhile, both theoretical calculation and weapon muzzle smoke test also demonstrated the lowering of smoke generation during propellant combustion. This phthalates‐free strategy paves a new way to eliminate potential hazards associated with phthalates during traditional gun propellant production and application.
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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".