Hydrogen adsorption on magnesium-decorated (3, 3) and (5, 0) boron nitride nanotubes
Bibliographic record
Abstract
Hydrogen is one of the cleanest ways to store energy in a post-fossil fuel economy. However, it can be dangerous as bulk gas and additional methods for hydrogen storage are needed. Physisorption on graphene sheets and nanotubes has been proposed as an effective approach due to their exceedingly high surface area and storage capacity similar to, or exceeding, highly compressed gas. Magnesium-doping has been demonstrated to significantly enhance hydrogen storage on boron-doped graphene sheets, but Mg-doped boron nitride nanotubes (BNNT), a potentially far more promising material due to the inherent dipoles in the surface providing stronger affinity for hydrogen, remain unexplored. In this in silico investigation, both the armchair (3,3) and zigzag (5,0) BNNT architectures, doped with Mg atoms, were examined for hydrogen storage capacity using first-principles density functional theory. Our calculations revealed that highly Mg-doped armchair and zigzag polymorphs of BNNTs could adsorb up to 9.65 and 8.77 weight percent hydrogen respectively, above the targets sought by the US Department of Energy for future hydrogen storage materials.
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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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.003 |
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".