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Record W2942517617 · doi:10.26434/chemrxiv.8081777

Direct CO2 Electroreduction from Carbonate

2019· preprint· en· W2942517617 on OpenAlexaff
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

VenueChemRxiv · 2019
Typepreprint
Languageen
FieldEnergy
TopicCO2 Reduction Techniques and Catalysts
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsElectrolysisSyngasElectrolyteCarbonateUpgradeChemical engineeringElectrochemistryWaste managementPotassium carbonatePolymer electrolyte membrane electrolysisChemistryCatalysisProcess engineeringMaterials scienceElectrodeComputer scienceEngineering

Abstract

fetched live from OpenAlex

The process of CO2 valorization – all the way from capture/concentration of CO2 to its electrochemical upgrade - requires significant inputs in each of the capture, upgrade, and separation steps. The gas-phase CO2 feed following the capture-and-release stage and into the CO2 electroreduction stage produce a large waste of CO2 (between 80 and 95% of CO2 is wasted due to carbonate formation or electrolyte crossover) that adds cost and energy consumption to the CO2 management aspect of the system. Here we report an electrolyzer that instead directly upgrades carbonate electrolyte from CO2 capture solution to syngas, achieving 100% carbon utilization across the system. A bipolar membrane is used to produce proton in situ, under applied potential, which facilitates CO2 releasing at the membrane:catalyst interface from the carbonate solution. Using an Ag catalyst, we generate pure syngas at a 3:1 H2:CO ratio, with no CO2 dilution at the gas outlet, at a current density of 150 mA/cm2, and achieve a full cell energy efficiency of 35%. The direct carbonate cell was stable under a continuous 145 h of catalytic operation at ca. 180 mA/cm2. The work demonstrates that coupling CO2 electrolysis directly with a CO2 capture system can accelerate the path towards viable CO2 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 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 categoriesMeta-epidemiology (narrow)
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 score0.162
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.013
GPT teacher head0.240
Teacher spread0.227 · 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.

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

Citations2
Published2019
Admission routes1
Has abstractyes

Explore more

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