Catalyst Aggregation Matters for Immobilized Molecular CO<sub>2</sub>RR Electrocatalysts
Bibliographic record
Abstract
Here, we detail how the catalytic behavior of immobilized molecular electrocatalysts for the CO 2 reduction reaction (CO 2 RR) can be impacted by catalyst aggregation. Operando Raman spectroscopy was used to study the CO 2 RR mediated by a layer of cobalt phthalocyanine (CoPc) immobilized on the cathode of an electrochemical flow reactor. We demonstrate that during electrolysis, the oxidation state of CoPc in the catalyst layer is dependent upon the degree of catalyst aggregation. Our data indicate that immobilized molecular catalysts must be dispersed on conductive supports to mitigate the formation of aggregates and produce meaningful performance data. We leveraged insights from this mechanistic study to engineer an improved CO-forming immobilized molecular catalyst─cobalt octaethoxyphthalocyanine (EtO 8 –CoPc)─that exhibited high selectivity (FE CO ≥ 95%), high partial current density ( J CO ≥ 300 mA/cm 2 ), and high durability (ΔFE CO < 0.1%/h at 150 mA/cm 2 ) in a flow cell. This work demonstrates how to accurately identify CO 2 RR active species of molecular catalysts using operando Raman spectroscopy and how to use this information to implement improved molecular electrocatalysts into flow cells. This work also shows that the active site of CoPc during CO 2 RR catalysis in a flow cell is the metal center.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".