Enhanced Effect of an External Electric Field on NH<sub>3</sub>BH<sub>3</sub> Dehydrogenation: an AIMD Study for Thermolysis
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide How to improve the dehydrogenation properties of ammonia borane (AB, NH 3 BH 3 ) is always a challenge for its practical application in hydrogen storage. In this study, we reveal the enhanced effect of an external electric field ( E ext ) on AB dehydrogenation by means of the ab initio molecular dynamics method. The molecular rotation induced by an electrostatic force can facilitate the formation of the H–N···B–H framework, which would aggregate into poly-BN species and further suppress the generation of the volatile byproducts. Meanwhile, the dihydrogen bond (N–H δ+ ··· δ− H–B) is favorably formed under E ext, and the interaction between relevant H atoms is enhanced, leading to a faster H 2 liberation. Correspondingly, the apparent activation energy for AB dissociation is greatly reduced from 18.42 to around 15 kcal·mol –1 with the application of an electric field, while that for H 2 formation decreases from 20.4 to about 16 kcal·mol –1 . In the whole process, the cleavage of the B–H bond is more favorable than that of the N–H bond, no matter whether the application of E ext . Our results give a deep insight into a positive effect of an electric field on AB dehydrogenation, which would provide an important inspiration for hydrogen storage in industry applications.
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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) | 0.000 | 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) | 0.004 | 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".