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Record W3025981599 · doi:10.1149/ma2020-0112940mtgabs

Fast and Selective CO<sub>2</sub> Reduction into CO with Cobalt Phthalocyanine in a Flow Cell

2020· article· en· W3025981599 on OpenAlexaff
Dorian Joulié, Shaoxuan Ren, Danielle A. Salvatore, Kristian Torbensen, Min Wang, Marc Robert, Curtis P. Berlinguette

Bibliographic record

VenueECS Meeting Abstracts · 2020
Typearticle
Languageen
FieldEnergy
TopicCO2 Reduction Techniques and Catalysts
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsOverpotentialCatalysisFaraday efficiencyElectrochemical reduction of carbon dioxideElectrolysisCobaltCarbon monoxideCarbon fibersSyngasMaterials scienceChemistryChemical engineeringInorganic chemistryElectrochemistryElectrodeOrganic chemistry

Abstract

fetched live from OpenAlex

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

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.009
GPT teacher head0.226
Teacher spread0.217 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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