Dramatic Improvement of Homogeneous Carbon Dioxide and Bicarbonate Electroreduction Using a Tetracationic Water-Soluble Cobalt Phthalocyanine
Bibliographic record
Abstract
Electrochemical conversion of carbon dioxide (CO 2 ) offers the opportunity to transform a greenhouse gas into valuable starting materials, chemicals, or fuels. Since many CO 2 capture strategies employ aqueous alkaline solutions, there is interest in catalyst systems that can act directly on such capture solutions. Herein, we demonstrate new catalyst designs where the electroactive molecules readily mediate the CO 2 -to-CO conversion in aqueous solutions between pH 4.5 and 10.5. Likewise, the production of CO directly from 2 M KHCO 3 solutions (pH 8.2) is possible. The improved molecular architectures are based on cobalt(II) phthalocyanine and contain four cationic trimethylammonium groups that confer water solubility and contribute to the stabilization of activated intermediates via a concentrated positive charge density around the active core. Turnover frequencies larger than 10 3 s –1 are possible at catalyst concentrations of down to 250 nM in CO 2 -saturated solutions. The observed rates are substantially larger than the related cobalt phthalocyanine-containing catalysts. Density functional theory calculations support the idea that the excellent catalytic properties are attributed to the ability of the cationic groups to stabilize CO 2 -bound reduced intermediates in the catalytic cycle. The homogeneous, aqueous CO 2 reduction that these molecules perform opens new frontiers for further development of the CoPc platform and sets a greatly improved baseline for CoPc-mediated CO 2 upconversion. Ultimately, this discovery uncovers a strategy for the generation of platforms for practical CO 2 reduction catalysts in alkaline solutions.
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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".