Recent advancements in tuning the electronic structures of transitional metal oxides for the electrocatalytic oxygen evolution reaction
Bibliographic record
Abstract
The smooth transition from finite non-renewables to renewable energy conversion technologies will require efficient electrocatalysts which can harness intermittent energies to store in the form of chemical bonds. The oxygen evolution reaction (OER) impedes the widespread usage of water electrolyzers to convert H2O into H2 and persists as a bottleneck, including other energy conversion devices with sluggish four H+/e− kinetics. In this context, designing highly active and stable catalysts capable of driving a lower overpotential in the OER to produce continuous hydrogen (H2) is a primary demanded. This chapter discussed the mechanism of the OER in conventional adsorbate oxygen and lattice oxygen participation in transition metal oxides (TMOs). Further, the influences of surface engineering, doping, and defects in the TMOs and understanding the electronic structure to screen electrodes towards the structure–activity relationship are highlighted. Specifically, the adsorption strength of O 2p is understood in detail as its binding ability over the surface of TMOs can be correlated directly to the OER activity. The iterative development of TMOs in terms of understanding electronic structural attributes is essential for the commercial deployment of energy conversion technologies. The comprehensive outlook of this chapter investigates thoroughly how TMOs can be used as significant materials for the OER in the near future.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.007 |
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".