In Situ Spectroscopic Methods for Electrocatalytic CO2 Reduction
Bibliographic record
Abstract
Electrochemical reduction of CO2 to value-added chemicals and fuels is a promising approach to store renewable energy while closing the anthropogenic carbon cycle. Despite significant advances in developing new electrocatalysts, this system still lacks enough energy conversion efficiency to become a viable technology for industrial applications. To develop an active and selective electrocatalyst and engineer the reaction environment to achieve high energy conversion efficiency, we need to improve our knowledge of the reaction mechanism and material structure under reaction conditions. In situ spectroscopies are among the most powerful tools which enable measurements of the system under real conditions. These methods provide information about reaction intermediates and possible reaction pathways, electrocatalyst structure and active sites, as well as the effect of the reaction environment on products distribution. This review aims to highlight the utilization of in situ spectroscopic methods that enhance our understanding of the CO2 reduction reaction. Infrared, Raman, X-ray absorption, X-ray photoelectron, and mass spectroscopies are discussed here. The critical challenges associated with current state-of-the-art systems are identified and insights on emerging prospects are discussed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".