Fast and Selective CO<sub>2</sub> Reduction into CO with Cobalt Phthalocyanine in a Flow Cell
Bibliographic record
Abstract
A balance between greenhouse gases production and capture is required to tackle the global warming resulting from them. Carbon dioxide (CO2) is considered as a waste gas but it is also a potential feedstock. Indeed, with the utilization of renewable energies, carbon dioxide electroreduction reaction (CO2RR) can generate useful products within a carbon neutral strategy. Practical CO2 conversion requires catalysts capable of mediating the efficient formation of a single product at high current densities over a long time (1). Among the potential products, carbon monoxide (CO) requires only 2 electrons to be generated which facilitate to reach high faradaic efficiencies. CO is also an important precursor in the chemical industry and its market is large enough to receive this new production method. (2) High current densities have been hit with copper-based catalysts but these catalysts tend to be nonspecific to only one product. Solid-state catalysts struggle to maintain a relevant selectivity at low overpotentials. They also rely mostly on noble metals or use deposition methods that are not upscalable for an industrial use. Molecular catalysts can be tuned to be highly selective towards CO at low overpotential but cannot operate CO2RR at relevant current densities for commercial purposes. Best molecular catalysts are close to 100 % faradaic efficiency for CO but the strongest production rate reported is 33 mA/cm² (3). This current density is more than 5 times smaller than the industrial relevant scale. Moreover, these catalysts are usually fragile over time with reported electrolysis less than ten hours. In this presentation, we will show that Cobalt Phthalocyanine (CoPc) - a widely available and known molecular catalyst - is able to compete with solid-state catalysts. We will illustrate how the CO2 delivery to a gas diffusion electrode can increase the reactivity of a molecular catalyst. We succeed to operate CO2RR with 95% faradaic efficiency for CO at 150 mA/cm² and an overall cell potential of 2.45 V. (4) We will also emphasize how the tunability of such catalysts can lower the reaction overpotential and increase the stability. (5) 1: Ind. Eng. Chem. Res. 2018, 57, 2165−2177 2: Acc. Chem. Res. 2018, 51, 4, 910-918 3: ACS Energy Lett. 2018, 3, 10, 2527-2532 4: Science 2019, 365, 6451, 367–369 5: Nature comm. 2019, 10, 3602
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".