Engineering the Oxygen Vacancies in Na <sub>2</sub> Ti <sub>3</sub> O <sub>7</sub> for Boosting Its Catalytic Performance in MgH <sub>2</sub> Hydrogen Storage
Bibliographic record
Abstract
Development of catalysts with highly efficient catalytic performance has a significant status in accelerating the MgH 2 hydrogen storage system. In this work, we report a Na 2 Ti 3 O 7 catalyst with rich oxygen vacancies (Na 2 Ti 3 O 7 -O v ), which was synthesized from Ti 3 C 2 -MXene, and confirm a remarkable enhancement to the hydrogen storage performance of MgH 2 . Expressly, the initial dehydrogenation temperature of the MgH 2 + 5Na 2 Ti 3 O 7 -O v (an addition of 5 wt % Na 2 Ti 3 O 7 -O v ) composite reduced substantially from 287 °C (for MgH 2 ) to 183 °C. Additionally, the MgH 2 + 5Na 2 Ti 3 O 7 -O v composite presented fast hydrogen ab/desorption kinetics and excellent reversible hydrogen storage performance with a retention rate of 90.1% after 10 cycles. Both experimental and theoretical calculations data verified that the oxygen vacancies in Na 2 Ti 3 O 7 -O v reduce the reaction activation energy during MgH 2 dehydrogenation and then convey an excellent hydrogen storage kinetics. This work provides a new design for advanced defect-based nanocatalysts for the MgH 2 hydrogen storage system.
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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".