<i>Operando</i> spectroscopy of nanoscopic metal/covalent organic framework electrocatalysts
Bibliographic record
Abstract
Metal and covalent organic frameworks (MOFs and COFs) are increasingly finding exceptional utility in electrocatalytic systems. Their chemically defined porous nature grants them key functions that may enhance their electrocatalytic performance relative to conventional molecular or heterogeneous materials. In order to obtain insights into their function, mechanism, and dynamics under electrocatalytic conditions, operando spectroscopy, that which is performed as the catalyst is functioning, has been increasingly applied. This mini review highlights several key works emerging in recent years that have used various operando spectroscopic techniques, namely UV-vis absorption, Raman, Infrared, and X-ray absorption spectroscopy, to investigate electrocatalytic MOFs and COFs. A brief introduction to each technique and how it was applied to investigate MOF/COF-based electrolytic systems is detailed. The unique set of data obtained, interpretations made, and progress attained all point to the power of operando spectroscopy in truly opening the functionality of MOFs and COFs across many aspects of catalysis.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".