Gradient Denitration Strategy Eliminates Phthalates Associated Potential Hazards During Gun Propellant Production and Application
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 it