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Record W4324348881 · doi:10.1002/cctc.202201514

Insight into Electrochemical Promotion of Cu/Co<sub>3</sub>O<sub>4</sub> Catalysts for the Reverse Water Gas Shift Reaction

2023· article· en· W4324348881 on OpenAlexaff
Ju Wang, Martin Couillard, Elena A. Baranova

Bibliographic record

VenueChemCatChem · 2023
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsNational Research Council CanadaUniversity of Ottawa
Fundersnot available
KeywordsCatalysisWater-gas shift reactionElectrochemistryCopperX-ray photoelectron spectroscopyCyclic voltammetryPolarization (electrochemistry)Materials scienceElectrodeOpen-circuit voltageInorganic chemistryAnalytical Chemistry (journal)ChemistryChemical engineeringPhysical chemistryVoltageMetallurgyEnvironmental chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract In this study, Copper‐modified Co 3 O 4 (Cu/Co 3 O 4 ) catalysts were investigated for the reverse water gas shift (RWGS) reaction at 200–400 °C under different CO 2 : H 2 ratios. It was found that the electrocatalytic performance of Co 3 O 4 was improved by loading 4 weight percent (wt.%) Cu nanoparticles (average size: 13 nm), which could be then used as an electrode in electrochemical promotion of catalysis (EPOC) studies. Under the applied voltages of +2 V and −1 V, the catalytic rates were suppressed and increased by about 40 % and 14 %, respectively. This was due to the changes in the active oxidation states of Cu and Co caused by O 2− migration under polarization, as confirmed by XPS, XRD, and cyclic voltammetry (CV). The study also revealed that the exchange current density (i 0 ) was counter‐correlated with the open‐circuit catalytic rate (r 0 ) and could serve as an informative tool for predicting the catalytic rate, which was demonstrated for the first time in the instance of RWGS 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.

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)0.001
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 score0.010
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.012
GPT teacher head0.229
Teacher spread0.217 · 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

Citations8
Published2023
Admission routes1
Has abstractyes

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