Investigation on Hygroscopicity of Low Sensitive Gun Propellant Based on Ladder‐Structured Nitrocellulose
Bibliographic record
Abstract
ABSTRACT Hygroscopicity plays a crucial role in the application of gun propellant. However, hygroscopicity of low sensitive gun propellant based on ladder‐structured nitrocellulose (LNC propellants) was not clear yet. In this work, moisture absorption behavior and hygroscopic mechanism of LNC propellants were studied by dynamic vapor sorption (DVS) instrument and molecular dynamics (MD) simulation. The DVS result shows that the capacity of moisture absorption for LNC propellants is obviously decreased by 60.60% (from 0.99% to 0.39% at RH of 50%) compared with propellants based on conventional nitrocellulose (NC), showing that moisture absorption behavior of low sensitive gun propellants can be inhibited well after the introduction of LNC. The Peleg absorption model provides a more accurate representation of the hygroscopic properties of LNC propellant, which can effectively predict the variation of moisture content throughout the moisture absorption process. Moreover, MD simulation results show that LNC has a weaker interaction with H2O molecule due to the grafting reaction of lots of hydroxyl group compared with NC, leading to lower hygroscopicity of LNC propellants. These results are expected to boost the practical application of low sensitive gun propellants containing LNC into the artillery.
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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".