Coordination-Adaptive Molecular Platform for Catalytic Water Oxidation
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Enzymes utilize adaptive frameworks to accommodate diverse structural and electronic requirements of intermediates, enhancing the catalysis. This Review outlines efforts to construct adaptive artificial molecular water oxidation catalysts, mainly based on ruthenium complexes, in a biomimetic manner. We initiated by discussing the importance of the coordinating ability of anionic ligands in the low oxidation state, in the aspect of water coordination feasibility, active species generation, and charge accumulation prevention via proton-coupled electron transfer (PCET). We then illustrated the introduction of additional ligands in the oxidized state, which helps to increase the electron density of the catalytic site, thereby lowering the overpotential. Beyond the first coordination sphere, we then discussed the effort of adjusting the identities of pendant base groups and introducing controlled microenvironments close to the catalytic sites, in order to regulate the key proton transfer. In regard to mechanistic understanding, the isolation of catalytic intermediates is also discussed, with particular emphasis on intermediates during the transition between 6- and 7-coordinate states. Applying the adaptive molecular platform to other metals, we conclude by prospecting the design principles of nonprecious metal-based water oxidation catalysts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".