Proton-donating cations enable efficient and stable acidic CO2 reduction in membrane electrode assemblies
Bibliographic record
Abstract
ABSTRACT Electrochemical CO2 reduction (CO2R) in acidic membrane electrode assemblies (MEAs) represents a promising pathway for sustainable chemical production, but achieving high selectivity, low cell voltage and long-term stability remains challenging. Current approaches using alkali cations can promote selectivity through cationic effects, but relying on H2O as a weak proton donor results in high overpotential and severe precipitation, causing elevated cell voltage and poor operational stability. Here, we introduce NH4+ as a proton-donating cation that simultaneously addresses these challenges in acidic MEAs. As a cation, it electromigrates to the catalyst surface, stabilizing *CO2 intermediates and reducing localized H+ concentration for high selectivity. As a proton donor, it provides superior proton-donating ability compared to H2O when H+ mass transport is limited, which decreases the protonation barrier and reduces CO2R overpotential on CoPc@CNT, resulting in a lower cell voltage. Furthermore, NH4+ effectively donates protons to bicarbonate, promoting its decomposition at significantly lower temperatures compared to KHCO3, thereby enabling easy removal of precipitates through mild heating and maintaining an NH3/NH4+ recirculation system for operational stability. As a result, this approach achieves an average CO2-to-CO selectivity of 86% in acidic MEAs at 100 mA cm−2 and 60°C using CoPc@CNT–NH2 catalyst, with stable performance over 110 h at an average cell voltage of 2.84 V, corresponding to a 40.6% energy efficiency. This strategy advances acidic MEA-based CO2R toward practical implementation by simultaneously achieving high selectivity, low overpotential and stable operation.
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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) | 0.001 | 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".