Novel Fluorine-containing Energetic Materials: How Potential are They? A Computational Study of Detonation Performance
Bibliographic record
Abstract
Abstract High energy density materials (HEDMs) have emerged as a research focus due to their advantageous ultra-high detonation pressure and velocity. The key objective of this study is to design materials with the best performance. Density functional theory (DFT) was utilized to evaluate the geometric structure, energy properties, and sensitivities of 28 designed F-containing derivatives. The theoretical density (ρ) and heat of formation (HOF) were used to estimate the detonation velocity ( D ) and pressure ( P ) of the title compounds. Our study shows that the introduction of fluorine-containing substituents or fluorine-free substituents into the CHOFN backbone or the CHON backbone can significantly enhance the detonation performance of derivatives. Among them, derivative B1 exhibites the best overall performance, including superior density, detonation performance, and sensitivity ( P = 58.89 GPa, D = 8.02 km/s, ρ = 1.93 g/cm³, and characteristic height H 50 = 34.6 cm). Our molecular design strategy contributes to the development of more novel HEDMs with excellent detonation performance and stability. It also marks a significant step towards a material engineering era guided by theory-based rational design. Methods For an accurate analyze, we employed the B3LYP functional with the 6-31+G(d,p) basis set for geometry optimization and exploration of physicochemical properties of the materials. The minima with no imaginary frequencies were confirmed using harmonic vibrational frequency results at the same theory level. With the assistance of DFT calculation, the quantum properties of the materials were analyzed using the Chapman-Jouguet (C-J) thermodynamic detonation theory. Our broad analysis facilitated an extensive assessment of these properties.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".