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Record W2945075429 · doi:10.1021/acsenergylett.9b00975

CO <sub>2</sub> Electroreduction from Carbonate Electrolyte

2019· article· en· W2945075429 on OpenAlexafffund
Yuguang Li, Geonhui Lee, Tiange Yuan, Ying Wang, Dae‐Hyun Nam, Ziyun Wang, F. Pelayo Garcı́a de Arquer, Yanwei Lum, Cao‐Thang Dinh, Oleksandr Voznyy, Edward H. Sargent

Bibliographic record

VenueACS Energy Letters · 2019
Typearticle
Languageen
FieldEnergy
TopicCO2 Reduction Techniques and Catalysts
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institute for Advanced Research
KeywordsSyngasElectrolysisElectrolyteCarbonateElectrochemistryChemical engineeringCatalysisUpgradeChemistryMaterials scienceWaste managementElectrodeOrganic chemistryComputer scienceEngineering

Abstract

fetched live from OpenAlex

The process of CO 2 valorization—from capture of CO 2 to its electrochemical upgrade—requires significant inputs in each of the capture, upgrade, and separation steps. Here we report an electrolyzer that upgrades carbonate electrolyte from CO 2 capture solution to syngas, achieving 100% carbon utilization across the system. A bipolar membrane is used to produce proton in situ to facilitate CO 2 release at the membrane:catalyst interface from the carbonate solution. Using a Ag catalyst, we generate syngas at a 3:1 H 2:CO ratio, and the product is not diluted by CO 2 at the gas outlet; we generate this pure syngas product stream at a current density of 150 mA/cm 2 and an energy efficiency of 35%. The carbonate-to-syngas system is stable under a continuous 145 h of catalytic operation. The work demonstrates the benefits of coupling CO 2 electrolysis with a CO 2 capture electrolyte on the path to practicable CO 2 conversion technologies.

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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.004
GPT teacher head0.193
Teacher spread0.189 · 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

Citations260
Published2019
Admission routes2
Has abstractyes

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