Damage Management in Water-Oxidizing Catalysts: From Photosystem II to Nanosized Metal Oxides
Bibliographic record
Abstract
Current energy resources largely rely on fossil fuels that are expected to be depleted in 50–200 years. On a global scale, the intensive use of this energy source has resulted in highly detrimental effects to the environment. Hydrogen production by water splitting, with sunlight as the main energy source, is a promising way to augment the production of renewable energy; however, the development of an efficient and stable water-oxidizing catalyst remains a key task before a technological breakthrough based on water splitting can be realized. A main issue hampering the development of commercially viable, non-precious-metal-based catalysts is their susceptibility to degradation. To efficiently address this major drawback, self-healing catalysts that can repair their structure without human intervention will be necessary. In this review, we focus on water oxidation by natural and artificial Mn-, Co-, and Ni-based catalysts and then discuss the self-healing properties that contribute to sustaining their catalytic activity.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".