Computational Engineering of Non‐van der Waals 2D Magnetene for Enhanced Oxygen Evolution and Reduction Reactions
Bibliographic record
Abstract
Abstract Non‐van der Waals two‐dimensional materials containing exposed transition metal atoms are promising catalysts for green energy storage and conversion. For instance, hematene and ilmenene have been successfully applied as catalysts. Building on these reports, this work is the first investigation of recently synthesized magnetene towards the Oxygen Evolution Reaction (OER) and Oxygen Reduction Reaction (ORR). Using Density Functional Theory (DFT) calculations, we unveil the mechanism, performance and ideal conditions for OER and ORR on magnetene. With overpotentials of η OER =0.50 V and η ORR =0.41 V, the material is not only a bifunctional catalyst, but also superior to state‐of‐the‐art systems such as Pt and IrO 2 . Additionally, its catalytic properties can be further enhanced through engineering strategies such as point defects and in‐plane compression. It reaches η ORR =0.28 V at a compressive strain of only 2 %, while the presence of Ni boosts it to η OER =0.39 V and η ORR =0.31 V, comparable to many reported single‐atom catalysts. Overall, this work demonstrates that magnetene is a promising bifunctional catalyst for applications such as regenerative fuel cells and metal‐air batteries.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".