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Record W3083766566 · doi:10.1021/acsenergylett.0c01606

pH Matters When Reducing CO<sub>2</sub> in an Electrochemical Flow Cell

2020· article· en· W3083766566 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueACS Energy Letters · 2020
Typearticle
Languageen
FieldEnergy
TopicCO2 Reduction Techniques and Catalysts
Canadian institutionsCanadian Institute for Advanced ResearchUniversity of British Columbia
FundersNational Research Council CanadaCanada Foundation for InnovationCanada Research ChairsCanadian Institute for Advanced Research
KeywordsElectrocatalystElectrolysisCatalysisElectrochemistryCurrent densityInorganic chemistryChemistryRedoxBicarbonateSelectivityChemical engineeringElectrodeAnalytical Chemistry (journal)Physical chemistryChromatographyOrganic chemistry

Abstract

fetched live from OpenAlex

The pH at the electrocatalyst surface plays a key role in defining the activity and selectivity of the CO2 reduction reaction (CO2RR). We report here operando Raman measurements of the catalyst surface in a customized CO2RR flow cell that enable the measure of pH. Using this flow cell, we were able to measure surface pH as a function of time, current density, and proximity to the catalyst surface during the electrolysis of bicarbonate solutions. We observed that increasing the current density from 0 to 200 mA cm–2 increased the surface pH from 8.5 to 10.3. We also show here that operation at elevated temperatures (70 °C) results in an increased surface pH and serves to suppress the competing and undesirable hydrogen evolution reaction.

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.

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 score0.028
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 integrity0.0000.000
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 head0.009
GPT teacher head0.207
Teacher spread0.199 · 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