Tailoring the capability of carbon nitride (C <sub>3</sub> N) nanosheets toward hydrogen storage upon light transition metal decoration
Bibliographic record
Abstract
Abstract To nurture the full potential of hydrogen (H 2 ) as a clean energy carrier, its efficient storage under ambient conditions is of great importance. Owing to the potential of material-based H 2 storage as a promising option, we have employed here first principles density functional theory calculations to study the H 2 storage properties of recently synthesized C 3 N monolayers. Despite possessing fascinating structural and mechanical properties C 3 N monolayers weakly bind H 2 molecules. However, our van der Waals corrected simulations revealed that the binding properties of H 2 on C 3 N could be enhanced considerably by suitable Sc and Ti doping. The stabilities of Sc and Ti dopants on a C 3 N surface has been verified by means of reaction barrier calculations and ab initio molecular dynamics simulations. Upon doping with C 3 N, the existence of partial positive charges on both Sc and Ti causes multiple H 2 molecules to bind to the dopants through electrostatic interactions with adsorption energies that are within an ideal range. A drastically high H 2 storage capacity of 9.0 wt% could be achieved with two-sided Sc/Ti doping that ensures the promise of C 3 N as a high-capacity H 2 storage 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) | 0.000 | 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".