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Record W3202829898 · doi:10.26434/chemrxiv.12891071.v1

Metallic Porous Electrodes Enable Efficient Bicarbonate Electrolysis

2020· preprint· en· W3202829898 on OpenAlexafffund
Zishuai Zhang, Faezeh Habibzadeh, Danielle A. Salvatore, Shaoxuan Ren, Eric W. Lees, Curtis P. Berlinguette

Bibliographic record

VenueChemRxiv · 2020
Typepreprint
Languageen
FieldEnergy
TopicCO2 Reduction Techniques and Catalysts
Canadian institutionsCanadian Institute for Advanced ResearchUniversity of British Columbia
FundersNatural Resources CanadaCanada First Research Excellence FundUniversity of British ColumbiaCanada Research Chairs
KeywordsElectrolysisElectrolyteFaraday efficiencyBicarbonateChemical engineeringMaterials scienceElectrolytic processElectrolytic cellPolymer electrolyte membrane electrolysisCathodeCarbon fibersElectrodeProcess engineeringChemistryComposite material

Abstract

fetched live from OpenAlex

We demonstrate here that a porous free-standing silver foam cathode in an electrolytic flow cell mediates efficient electrolysis of 3.0 M bicarbonate solutions into CO. These results have direct implications for carbon capture schemes where OH- solutions react with CO2 to form bicarbonate-rich solutions that need to be treated to recycle the sorbent and recover the CO2. Our study shows a viable path for replacing the high-temperature thermal process currently used to recover CO2 from these carbon capture solutions by using electricity to drive the conversion of bicarbonate into CO2 and subsequently into CO. The use of free-standing porous silver electrodes was found to yield electrolysis performance parameters (e.g., a Faradaic efficiency for CO production, FECO, of 78% at 100 mA cm2; <3% performance loss after 80 h operation) that are superior to results obtained in bicarbonate electrolyzers that utilize conventional carbon-based gas diffusion electrodes (GDEs) designed for gaseous CO2 fed electrolyzers. These performance metrics are comparable to any electrolytic flow cell fed directly with a CO2 feedstock, with the added benefit of not requiring an energy-intensive pressurization step that would be necessary for the electrolysis of gaseous CO2. These findings represent a potentially important step in closing the carbon cycle.

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 score0.061
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.015
GPT teacher head0.239
Teacher spread0.224 · 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

Citations3
Published2020
Admission routes2
Has abstractyes

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