Energetic Molecules Encapsulated Inside Carbon Nanotubes and between Graphene Layers: DFT Calculations
Bibliographic record
Abstract
Insensitive energetic materials are desirable for propellants because of the reduced risks involved with their use. The ability to control the decomposition pathways for such materials is also of interest since it leads to optimal performance and controlled energy release. With these goals in mind, molecular structure and total energy calculations are used to investigate the confinement of energetic molecules inside carbon nanostructures. The molecules considered were FOX-7 (1,1-diamino-2,2-dinitroethylene), RDX (hexahydro-1,3,5-trinitro-striazine), HMX (octahydro-1,3,5,7-tetranitro-1,3,5,7-tetrazocine), DHT (3,6-di(hydrazino)-1,2,4,5-tetrazine), DiAT (3,6-diazido-1,2,4,5-tetrazine), DAAT (3,3′-azo-bis(6-amino-1,2,4,5-tetrazine)), and five different N -oxides of DAAT (DAATO n, with n = 1–5). Each of the eleven molecules is encapsulated inside a carbon nanotube (CNT) in order to determine if it is stabilized from such confinement. The calculations predict that each molecule could be stabilized by 32–53 kcal/mol if a CNT of appropriate size is used. FOX-7, RDX, and HMX were also confined between graphene layers, resulting in these molecules being stabilized by 28–40 kcal/mol. The stabilization stems from dispersion interactions between the molecules and carbon nanostructures, Coulombic interactions due to charge transfer, and intermolecular H-bonding in some cases. Overall, each molecule can be stabilized when encapsulated in a carbon nanostructure of appropriate size, thereby reducing its sensitivity.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".