SO2-Tolerant Electrocatalytic Reduction of CO2 from Simulated Industrial Flue Gas
Bibliographic record
Abstract
The electrochemical reduction of CO 2 using copper-based electrocatalysts offers a route to produce high-value multicarbon (C 2+ ) products from renewable electricity (Nat. Catal. 4, 952-958 (2021); Nature 614, 262-269 (2023)). To date, the efficient electrocatalytic conversion of CO 2 to multicarbon products has only been possible when using impurity-free CO 2 sources, such as from direct air capture. The generation of such high-grade CO 2 streams is expensive, accounting for almost half of the total energy required for both capture and electroreduction processes (Nat. Catal. 4, 952-958 (2021)). Conversely, capturing CO 2 from point sources, such as industrial flue gas, is more efficient due to the higher concentration of CO 2 in the feed. However, trace amounts of sulfur dioxide (10 ~ 400 ppm SO 2 ) inherently present in these streams will significantly degrade the CO 2 conversion process. All previous attempts to convert CO 2 with SO 2 present in the feed have resulted in immediate catalyst poisoning and an irreversible loss of CO 2 conversion activity. In this study, we designed a modified catalyst layer to react a stream of dilute CO 2 containing 400 ppm SO 2 to multicarbon products with high stability and performance metrics that match or exceed those achieved with pure CO 2 streams. Driven by density function theory and COMSOL simulations, we designed an ionomer:copper:polytetrafluoroethylene (PTFE) (ICP) electrode that features both hydrophobic and highly-charged hydrophilic domains to limit water adsorption and promote CO 2 over SO 2 transport near the electrochemically active sites. This deactivates the SO 2 poisoning mechanism, thus enabling stable and efficient CO 2 conversion (see figure). Our approach achieved a sustained C 2+ Faradaic efficiency (FE) of 50% for the initial 160 hours at 100 mA cm-2. In order to improve the overall C 2+ current efficiency (j C2+ ) towards industrial scales, we applied our strategy in high-surface-area copper electrodes. We achieved CO 2 conversion in the presence of 400 ppm SO 2 with a C 2+ FE of 76% at a current density of 700 mA cm-2, surpassing what can be achieved in existing integrated CO 2 capture-electrolysis systems that use pure CO 2 . Overall, our approach provides a fully 140-fold increase in performance (FE C2+ × j C2+ ) compared to the best prior CO 2 conversion systems with added SO 2 (Nat. Nanotechnol. doi: 10.1038/s41565-022-01286-y (2023); J. Am. Chem. Soc. 141, 9902-9909 (2019)). These findings represent an important advancement in the field of CO 2 conversion and highlight the potential of our strategy for industrial-scale applications. Figure 1
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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 | 0.001 |
| 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) | 0.000 | 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".