An Efficient Way To Suppress the Competition between Adsorption of H<sub>2</sub> and Desorption of <i>n</i>H<sub>2</sub>–Nb Complex from Graphene Sheet: A Promising Approach to H<sub>2</sub> Storage
Bibliographic record
Abstract
We performed first-principles calculations to investigate the electronic structure and hydrogen storage capacity of bare niobium (Nb) and niobium-decorated graphene (GR@Nb). The results predict that bare Nb can bind 6 H 2 molecules in the quasi-molecular form to reach saturation with the binding energy of hydrogen in the range 0.2–0.7 eV. In addition, the maximum temperature of desorption was 466 K. We demonstrated that the most favorable site for Nb atoms is the hollow site of graphene with a binding energy of 1.783 eV. Moreover, we analyzed the stability of Nb dopant on the graphene surface by means of reaction barriers calculations and an ab initio molecular dynamics simulation. Our calculations reveal that an energy barrier of 0.435 eV is required for a Nb atom to move from one hollow site to the adjacent hollow site, which is far greater than the energy of thermal vibration of Nb at 300 K. We show that Nb-decorated graphene doped with nitrogen atoms at 7.25% can absorb 12 H 2 molecules in the quasi-molecular form with an average binding energy of 0.410 eV at an average desorption temperature of 520 K. Our results predict that desorption of n H 2 –Nb complex from a graphene sheet can be suppressed by increasing the concentration of nitrogen atoms to 7.25%. Finally, the storage capacity of 2NGR@2Nb is about 8 wt %. These results clearly demonstrate that Nb-decorated graphene with a 7.25% concentration of nitrogen atoms is a promising candidate for H 2 storage for mobile 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) | 0.001 | 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".