Visualizing Electrochemical CO <sub>2</sub> Conversion via the Emerging Scanning Electrochemical Microscope: Fundamentals, Applications and Perspectives
Bibliographic record
Abstract
Abstract With the rapid development and maturity of electrochemical CO 2 conversion involving cathodic CO 2 reduction reaction (CO 2 RR) and anodic oxygen evolution reaction (OER), conventional ex situ characterizations gradually fall behind in detecting real‐time products distribution, tracking intermediates, and monitoring structural evolution, etc. Nevertheless, advanced in situ techniques, with intriguing merits like good reproducibility, facile operability, high sensitivity, and short response time, can realize in situ detection and recording of dynamic data, and observe materials structural evolution in real time. As an emerging visual technique, scanning electrochemical microscope (SECM) presents local electrochemical signals on various materials surface through capturing micro‐current caused by reactants oxidation and reduction. Importantly, SECM holds particular potentials in visualizing reactive intermediates at active sites and obtaining instantaneous morphology evolution images to reveal the intrinsic reactivity of active sites. Therefore, this review focuses on SECM fundamentals and its specific applications toward CO 2 RR and OER, mainly including electrochemical behavior observation on local regions of various materials, target products and onset potentials identification in real‐time, reaction pathways clarification, reaction kinetics exploration under steady‐state conditions, electroactive materials screening and multi‐techniques coupling for a joint utilization. This review undoubtedly provides a leading guidance to extend various SECM applications to other energy‐related fields.
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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) | 0.001 | 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".