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Record W4308544362 · doi:10.1021/acsami.2c14368

Electrochemical Reduction of Carbon Dioxide at TiO<sub>2</sub>/Au Nanocomposites

2022· article· en· W4308544362 on OpenAlexafffund
M. Nur Hossain, Rachelle M. Choueiri, Sharon Abner, Leanne D. Chen, Aicheng Chen

Bibliographic record

VenueACS Applied Materials & Interfaces · 2022
Typearticle
Languageen
FieldEnergy
TopicCO2 Reduction Techniques and Catalysts
Canadian institutionsUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of CanadaCanada Foundation for Innovation
KeywordsNanocompositeMaterials scienceElectrochemistryCatalysisChemical engineeringElectrochemical reduction of carbon dioxideAqueous solutionNanoparticleCrystalliteGalvanic cellCarbon monoxideNanotechnologyElectrodeOrganic chemistryPhysical chemistryChemistryMetallurgy

Abstract

fetched live from OpenAlex

Herein, we report on the facile synthesis of nanocomposite consisting of TiO2 and Au nanoparticles (NPs) via a tailored galvanic replacement reaction (GRR). The electrocatalytic activity of the synthesized TiO2/Au nanocomposites for CO2 reduction was investigated in an aqueous solution using various electrochemical methods. Our results demonstrated that the TiO2/Au nanocomposites formed through the GRR process exhibited improved catalytic activities for CO2 reduction, while generating more hydrocarbon molecules than the typical formation of CO in contrast to polycrystalline Au. GC analysis and NMR spectroscopy revealed that CO and CH4 were the gas products, whereas HCOO–, CH3COO–, CH3OH, and CH3CH2OH were the liquid products from the CO2 reduction at different cathodic potentials. This remarkable change was further studied using the density functional theory (DFT) calculations, showing that the TiO2/Au nanocomposites may increase the binding energy of the formed ·CO intermediate and reduce the free energy compared to Au, thus favoring the downstream generation of multicarbon products. The TiO2/Au nanocomposites have high catalytic activity and excellent stability and are easy to fabricate, indicating that the developed catalyst has potential application in the electrochemical reduction of CO2 to value-added products.

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 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 score0.003
Threshold uncertainty score0.956

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 head0.006
GPT teacher head0.212
Teacher spread0.206 · 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.

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

Citations19
Published2022
Admission routes2
Has abstractyes

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