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Record W3017828098 · doi:10.1016/j.isci.2020.101094

Bubble Formation in the Electrolyte Triggers Voltage Instability in CO2 Electrolyzers

2020· article· en· W3017828098 on OpenAlexafffund
ChungHyuk Lee, Benzhong Zhao, Jason Keonhag Lee, Kieran F. Fahy, Kevin M. Krause, Aimy Bazylak

Bibliographic record

VenueiScience · 2020
Typearticle
Languageen
FieldEnergy
TopicCO2 Reduction Techniques and Catalysts
Canadian institutionsCanadian Institute for Energy TrainingMcMaster UniversityUniversity of Toronto
FundersWestern Economic Diversification CanadaCanadian Institutes of Health ResearchNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsNational Research Council CanadaCanada Foundation for InnovationUniversity of Saskatchewan
KeywordsElectrolyteBubbleElectrochemistryElectrodeInstabilityChemical engineeringVoltageMaterials scienceChemistryLiquid bubbleNanotechnologyMechanicsElectrical engineeringPhysics

Abstract

fetched live from OpenAlex

The electrochemical reduction of CO 2 is promising for mitigating anthropogenic greenhouse gas emissions; however, voltage instabilities currently inhibit reaching high current densities that are prerequisite for commercialization. Here, for the first time, we elucidate that product gaseous bubble accumulation on the electrode/electrolyte interface is the direct cause of the voltage instability in CO 2 electrolyzers. Although bubble formation in water electrolyzers has been extensively studied, we identified that voltage instability caused by bubble formation is unique to CO 2 electrolyzers. The appearance of syngas bubbles within the electrolyte at the gas diffusion electrode (GDE)-electrolyte chamber interface (i.e. ∼10% bubble coverage of the GDE surface) was accompanied by voltage oscillations of 60 mV. The presence of syngas in the electrolyte chamber physically inhibited two-phase reaction interfaces, thereby resulting in unstable cell performance. The strategic incorporation of our insights on bubble growth behavior and voltage instability is vital for designing commercially relevant CO 2 electrolyzers.

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)0.001
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 score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0000.000
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 head0.015
GPT teacher head0.247
Teacher spread0.232 · 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

Citations60
Published2020
Admission routes2
Has abstractyes

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