Hazard Characterization of Uncoated and Coated Aluminium Nanopowder Compositions
Bibliographic record
Abstract
The thermal properties of various uncoated and coated aluminum nanopowders and their effects on the thermal stability, outgassing behavior, and electrostatic discharge sensitiveness of various energetic materials were studied. These aluminum nanopowders had a mean particle size of 20-120 nm. The coated samples had a layer of 7-25% mass of polymer. The thermal behavior of the aluminum nanopowders in air was determined, and the effects of the particle size and the coating on the reactivity of aluminum nanopowders are discussed. Aluminium nanopowders are very reactive in the presence of water, resulting in aging problems. The coating of polymer has a minor effect on the reactivity of aluminum nanopowders with water. On the other hand, the results from an aging study show that the coated aluminum nanopowder is more stable than the uncoated nanopowder in humid atmospheres. The addition of some coated aluminum nanopowders lowers the onset temperatures of cyclotrimethylenetrinitramine, trinitrotoluene, and glycidyl azide polymer by ∼20°C. Outgassing results obtained for various cyclotrimethylenetrinitramine/aluminum mixtures at 100° C show that the uncoated Al120 enhances the low-temperature solid-phase decomposition of cyclotrimethylenetrinitramine. The addition of uncoated aluminum nanopowders has previously been shown to increase the electrostatic discharge sensitiveness of both ammonium dinitramide and ammonium perchlorate to ignition energies that can easily be carried by a human body. In contrast, the coated aluminum nanopowders do not appear to sensitize ammonium dinitramide and ammonium perchlorate toward electrostatic discharge, which suggests that the coatings can effectively prevent the sensitization effect.
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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 | 0.001 | 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".