SENSITIVITY OF POLYMER-BONDED EXPLOSIVES FROM MOLECULAR MODELING DATA
Bibliographic record
Abstract
Sensitive energetic materials are an issue for military and civilian applications. To prevent undesired explosions, sensitive energetic materials are embedded in a protective polymer, resulting in polymer-bonded explosives (PBX). The appropriate polymer will absorb part of the energy caused by stimuli such as shock, impact, friction, and heat, thus decreasing sensitivity. To investigate how an appropriate polymer absorbs energy, three PBX models were simulated using molecular dynamics. The COMPASS force field implemented in the Materials Studio software was used. Molecular dynamics simulations were performed for three RDX-based formulations in which a single polymer chain (HTPB, Estane, or EVA) was placed at the boundary surface of an RDX crystal. Simulations were carried out at high temperature (700 K) and high pressure (15 GPa). The resulting models were analyzed in terms of potential energy increase, energy distribution, and values of the different potential energy contributions for RDX/HTPB, RDX/Estane, and RDX/EVA. The polymer binders HTPB, Estane, and EVA in such PBX formulations absorbed between 24% and 31% of internal energy, respectively, thereby making less sensitive PBXs formulations than pure RDX. This percentage is proposed as an indicator key for experimentalists to determine the most efficient polymer that can be used, for a given explosive, to minimize munition sensitivity. A clear correlation is established between the calculated absorption of internal energy by polymers and experimental sensitivity values for the three formulations studied under extreme experimental conditions. This approach may be applied to other new formulations prior to testing them in laboratories.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".