An Autonomously Self‐Healing Silicone Polyurea Waterproof Coating for Combustible Cartridge Cases
Bibliographic record
Abstract
Abstract Nitrocellulose‐based combustible cartridge case (NC‐3C) is highly hygroscopic, which greatly limits its broader application. The effective strategy for NC‐3Cs to prevent moisture is the endowment of waterproof coatings. However, the traditional waterproof coatings for NC‐3Cs cannot self‐heal the inevitable mechanical microcracks suffered during handling and storage, which often gives moisture much more opportunities to destroy the internals. Therefore, it is necessary to develop a self‐healing waterproof coating for the protection of NC‐3Cs. Herein, a smart self‐healing silicone polyurea (SPU) elastomer based on dynamic reversible hydrogen bonds is designed as a waterproof coating for NC‐3Cs. The SPU elastomer shows an outstanding self‐healing efficiency of 99.06 % after 12‐hour self‐healing at room temperature. And it also can self‐heal its damages even when the ambient temperatures rise to 50 °C or drop to −18 °C. Moreover, the SPU‐coated NC‐3C can also self‐heal its microcracks in time after being scratched and obtains extremely low water absorptions (<0.20 %) after 24‐hour soaking, exhibiting good waterproof performance. Endowing the waterproof coating with self‐healing ability is a promising design strategy to repair the coating microcracks and improve the environmental adaptability of NC‐3C during handling and storage. This work pioneers a new design of self‐healing waterproof coating for NC‐3Cs, and it has a positive guiding significance to expend broader application of NC‐3C.
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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".