What X-ray absorption spectroscopy can tell us about the active state of earth-abundant electrocatalysts for the oxygen evolution reaction
Bibliographic record
Abstract
Chemical energy storage is an attractive solution to secure a sustainable energy supply. It requires an electrocatalyst to be implemented efficiently. In order to rationally improve the electrocatalyst materials and thereby the reaction efficiency, one must reveal the nature of the electrocatalyst under reaction conditions, i.e., its active state. For a better understanding of earth-abundant metal oxides as electrocatalysts for the oxygen evolution reaction (OER), the combination of electrochemical (EC) methods and X-ray absorption spectroscopy (XAS) has been very insightful and still holds untapped potential. Herein, we concisely introduce the basics of EC and XAS and provide the necessary framework to discuss changes that electrocatalytic materials undergo, presenting manganese oxides as examples. Such changes may occur during preparation and storage, during immersion in an electrolyte, as well as during application of potentials without or with catalytic reactions. We conclude with a concise summary of how EC and XAS are currently combined to elucidate the active state as well as an outlook on future opportunities to understand the mechanisms of electrocatalysis using combined operando EC-XAS experiments.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".