SO2-Tolerant Electrocatalytic Reduction of CO2 from Simulated Industrial Flue Gas
Bibliographic record
Abstract
The electrochemical reduction of CO2 using copper-based electrocatalysts offers a route to produce high-value multicarbon (C2+) products from renewable electricity (Nat. Catal. 4, 952-958 (2021); Nature 614, 262-269 (2023)). To date, the efficient electrocatalytic conversion of CO2 to multicarbon products has only been possible when using impurity-free CO2 sources, such as from direct air capture. The generation of such high-grade CO2 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 CO2 from point sources, such as industrial flue gas, is more efficient due to the higher concentration of CO2 in the feed. However, trace amounts of sulfur dioxide (10 ~ 400 ppm SO2) inherently present in these streams will significantly degrade the CO2 conversion process. All previous attempts to convert CO2 with SO2 present in the feed have resulted in immediate catalyst poisoning and an irreversible loss of CO2 conversion activity. In this study, we designed a modified catalyst layer to react a stream of dilute CO2 containing 400 ppm SO2 to multicarbon products with high stability and performance metrics that match or exceed those achieved with pure CO2 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 CO2 over SO2 transport near the electrochemically active sites. This deactivates the SO2 poisoning mechanism, thus enabling stable and efficient CO2 conversion (see figure). Our approach achieved a sustained C2+ Faradaic efficiency (FE) of 50% for the initial 160 hours at 100 mA cm-2. In order to improve the overall C2+ current efficiency (jC2+) towards industrial scales, we applied our strategy in high-surface-area copper electrodes. We achieved CO2 conversion in the presence of 400 ppm SO2 with a C2+ FE of 76% at a current density of 700 mA cm-2, surpassing what can be achieved in existing integrated CO2 capture-electrolysis systems that use pure CO2. Overall, our approach provides a fully 140-fold increase in performance (FEC2+ × jC2+) compared to the best prior CO2 conversion systems with added SO2 (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 CO2 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 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) | 0.000 | 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 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".