Catalysts for electrochemical CO2 conversion: material sustainability perspective
Bibliographic record
Abstract
The electrochemical reduction of CO 2 (eCO 2 R) presents a promising pathway for addressing climate change by converting CO 2 into value-added chemicals and fuels. A crucial aspect of this technology is the choice of catalyst materials, which directly influences the selectivity, stability and sustainability of the process. Here we introduce a streamlined supply risk assessment coupled with life-cycle environmental impact associated with various catalysts used in eCO 2 R for products, including formate, carbon monoxide, ethylene, and ethanol to provide a well-rounded perspective for catalysts’ sustainability assessment. We compare more than 68 case studies in eCO 2 R using various metal-based catalysts. Our results show that Bi-based catalysts for formate production have the highest supply risk and environmental burdens, while Sn-based catalysts show overall better durability and much lower sustainability concerns. Copper-based catalysts’ supply risk for ethylene conversion is lower and more concentrated, whereas the supply risk for ethanol conversion is more dispersed. Our findings further confirm that improving catalyst performance—especially the stability—can substantially mitigate both supply risks and environmental impacts. This highlights the urgent need for standardized methodologies to assess catalyst stability and novel strategies to further improve catalyst stability using both material and system approaches. We call for stronger cross-sector collaboration to further integrate criticality and sustainability assessment frameworks with more granular datasets and dynamic spatial and temporal representation, for continuous eco-design improvement of eCO 2 R catalysts.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".