Computational insights into novel dicobalt polynitrogen: structure, stability, intermolecular interaction, and application
Bibliographic record
Abstract
Previous studies have suggested that polynitrogen species are significant as potential candidates for superior energetic material. In this paper, the polynitrogen species of Co2(N5)4 were reasonably designed and studied by the density functional theory (DFT), and five isomers of Co2(N5)4 were selected. These species were explored in detail, including structure, stability, intermolecular interaction, and application. The five isomers, each with its own special structure feature, were stable enough based on the analysis of bond energy, chemical hardness, and aromaticity. Furthermore, the intermolecular interactions suggested the presence of a covalent interaction in the Co–Co and N–N bonds, the electronic delocalization in cyclo-N5, and the ionic feature in the Co–N bond. In addition, all of the title species held high-energy content. Compared with the known high energy density materials of HB(N5)3Be2(N5)3BH, energetic material of nitromethane, and famous nitramine explosive HMX (1,3,5,7-tetranitro-1,3,5,7-tetrazocane), Co2(N5)4 holds a stronger advantage. The five Co2(N5)4 species were located at 27.8–35.8 kcal/mol per N2 unit, their energy densities were about 2.73 × 104 MJ/kg, and their mass densities were in the range of 2.60–2.74 g/cm3. Significantly, the 4-1 was the most stable, and its density was also the greatest among the five species. Thus, it has the most potential as a high energy density material.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".