Characterization of Aluminum Powders: II. Aluminum Nanopowders Passivated by Non‐Inert Coatings
Bibliographic record
Abstract
Abstract Results of DTA‐TG investigation and chemical analysis of electro‐exploded aluminum nanopowders, passivated and/or coated with the non‐inert reagents: nitrocellulose (NC), oleic acid (C 17 H 33 COOH) and stearic acid (C 17 H 35 COOH), which were suspended in kerosene and ethanol, amorphous boron, nickel, fluoropolymer, ethanol and air (for comparison), are discussed. Surface protection of aluminum nanopowders by coatings of different origin results in significant advantages in the energetic properties of the powders. Aluminum nanopowders with a protecting surface show increased stability to oxidation in nitrogen, air and in water during storage period. On the basis of the experimental results, a diagram of the formation and stabilization of the coatings is proposed. The kinetics of the interaction of aluminum nanopowders with nitrogen, air and water is discussed. Recommendations concerning the efficiency of non‐inert reagent passivation are proposed on the basis of comprehensive analysis of the experimental data.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".