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Record W4377940879 · doi:10.1038/s41467-023-38775-0

Pressure dependence in aqueous-based electrochemical CO2 reduction

2023· article· en· W4377940879 on OpenAlexafffund
Liang Huang, Ge Gao, Chaobo Yang, Xiaoyan Li, Rui Kai Miao, Yanrong Xue, Ke Xie, Pengfei Ou, Cafer T. Yavuz, Yu Han, Gaetano Magnotti, David Sinton, Edward H. Sargent, Lu Xu

Bibliographic record

VenueNature Communications · 2023
Typearticle
Languageen
FieldEnergy
TopicCO2 Reduction Techniques and Catalysts
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaKing Abdullah University of Science and Technology
KeywordsFormateCathodeElectrolysisElectrochemistryAmbient pressureSelectivityElectrolysis of waterMaterials scienceChemical engineeringAqueous solutionCatalysisInorganic chemistryChemistryElectrodeOrganic chemistryPhysical chemistry

Abstract

fetched live from OpenAlex

Abstract Electrochemical CO 2 reduction (CO 2 R) is an approach to closing the carbon cycle for chemical synthesis. To date, the field has focused on the electrolysis of ambient pressure CO 2 . However, industrial CO 2 is pressurized—in capture, transport and storage—and is often in dissolved form. Here, we find that pressurization to 50 bar steers CO 2 R pathways toward formate, something seen across widely-employed CO 2 R catalysts. By developing operando methods compatible with high pressures, including quantitative operando Raman spectroscopy, we link the high formate selectivity to increased CO 2 coverage on the cathode surface. The interplay of theory and experiments validates the mechanism, and guides us to functionalize the surface of a Cu cathode with a proton-resistant layer to further the pressure-mediated selectivity effect. This work illustrates the value of industrial CO 2 sources as the starting feedstock for sustainable chemical synthesis.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.732
Threshold uncertainty score0.469

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.016
GPT teacher head0.305
Teacher spread0.289 · 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 teacher head, 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

Citations117
Published2023
Admission routes2
Has abstractyes

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