Nanodispersed metal powders in high-energy condensed systems
Bibliographic record
Abstract
The results of earlier published Russian and foreign works concerning the use of nanodispersed metal powders in high-energy condensed systems (HECSs) are analyzed and generalized. Modern technological achievements allow one to produce powders of aluminum, boron, and other metals, as well as their alloys and oxides, and make them commercially available. This has resulted in a boom in studies on the possibility of improving HECS characteristics by introducing metal nanopowders in Russia, Europe, Canada, and the United States. The results of some works show that introducing nanopowders of alumina and other metals into rocket propellants, explosives, and pyrotechnic compositions increases their combustion rate and detonation properties. The composition of a double-base propellant (in which the liquid fuel contains 50–150-nm boron nanoparticles and a liquid mixture of hydrogen peroxide and ammonium nitrate is used as an oxidizer) and the composition of a solid rocket propellant based on polybutadiene with end hydroxyl groups, ammonium perchlorate, and 13.0–15.0% boron nanoparticles (which improve the combustion and increase the conversion of the propellant) are of some interest.
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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".