Experimental and Theoretical Insights into Single Atoms, Dual Atoms, and Sub‐Nanocluster Catalysts for Electrochemical CO<sub>2</sub> Reduction (CO<sub>2</sub>RR) to High‐Value Products
Bibliographic record
Abstract
Abstract Electrocatalytic carbon dioxide (CO 2 ) conversion into valuable chemicals paves the way for the realization of carbon recycling. Downsizing catalysts to single‐atom catalysts (SACs), dual‐atom catalysts (DACs), and sub‐nanocluster catalysts (SNCCs) has generated highly active and selective CO 2 transformation into highly reduced products. This is due to the introduction of numerous active sites, highly unsaturated coordination environments, efficient atom utilization, and confinement effect compared to their nanoparticle counterparts. Herein, recent Cu‐based SACs are first reviewed and the newly emerged DACs and SNCCs expanding the catalysis of SACs to electrocatalytic CO 2 reduction (CO 2 RR) to high‐value products are discussed. Tandem Cu‐based SAC–nanocatalysts (NCs) (SAC–NCs) are also discussed for the CO 2 RR to high‐value products. Then, the non‐Cu‐based SACs, DACs, SAC–NCs, and SNCCs and theoretical calculations of various transition‐metal catalysts for CO 2 RR to high‐value products are summarized. Compared to previous achievements of less‐reduced products, this review focuses on the double objective of achieving full CO 2 reduction and increasing the selectivity and formation rate toward C–C coupled products with additional emphasis on the stability of the catalysts. Finally, through combined theoretical and experimental research, future outlooks are offered to further develop the CO 2 RR into high‐value products over isolated atoms and sub‐nanometal clusters.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".