On the Mechanism of Efficiency of Lead Azide
Bibliographic record
Abstract
Abstract The mechanism of superior efficiency of lead azide (LA) in comparison with mercury fulminate (MF) is not understood. Indeed, both primary explosives have quite close detonation velocities and result in a large amount of condensed lead (Pb) and mercury (Hg), respectively. We explore an idea that the high efficiency of LA is due to the fact that the boiling point of lead markedly exceeds that of Hg. Then the products of LA in the detonation reaction zone could contain liquid Pb droplets, while MF products are gaseous. These lead droplets could violently impact the acceptor charge and favor its initiation. The plate dent studies of high explosives (HE) heavily loaded by metal particles provide an indirect support to the proposed mechanism. To check this hypothesis we numerically studied the donor/acceptor problem, where the donor is made of HE loaded with the inert metal particles. Pressure, velocity, and temperature relaxations of particles are taken into account. The model agrees with the experimental effect of metal addition on HE performance. However, the calculations show that the effect of particle penetration into the acceptor is late and weak in comparison with the effect of primary shock induced in the acceptor. Thus, the above hypothesis could not guide the development of green substitutes of LA. Hybrid mixtures of a nanothermite with a high explosive seem to be more promising for this purpose. A simple explanation is proposed for the superior triggering capacity of LA.
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) | 0.003 | 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".