Stabilizing *CO<sub>2</sub> Intermediates at the Acidic Interface using Molecularly Dispersed Cobalt Phthalocyanine as Catalysts for CO<sub>2</sub> Reduction
Bibliographic record
Abstract
Abstract CO2 electroreduction (CO2R) operating in acidic media circumvents the problems of carbonate formation and CO2 crossover in neutral/alkaline electrolyzers. Alkali cations have been universally recognized as indispensable components for acidic CO2R, while they cause the inevitable issue of salt precipitation. It is therefore desirable to realize alkali‐cation‐free CO2R in pure acid. However, without alkali cations, stabilizing *CO2 intermediates by catalyst itself at the acidic interface poses as a challenge. Herein, we first demonstrate that a carbon nanotube‐supported molecularly dispersed cobalt phthalocyanine (CoPc@CNT) catalyst provides the Co single‐atom active site with energetically localized d states to strengthen the adsorbate‐surface interactions, which stabilizes *CO2 intermediates at the acidic interface (pH=1). As a result, we realize CO2 conversion to CO in pure acid with a faradaic efficiency of 60 % at pH=2 in flow cell. Furthermore, CO2 is successfully converted in cation exchanged membrane‐based electrode assembly with a faradaic efficiency of 73 %. For CoPc@CNT, acidic conditions also promote the intrinsic activity of CO2R compared to alkaline conditions, since the potential‐limiting step, *CO2 to *COOH, is pH‐dependent. This work provides a new understanding for the stabilization of reaction intermediates and facilitates the designs of catalysts and devices for acidic CO2R.
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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".