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Record W4205134537 · doi:10.1002/cctc.202101873

Enhanced Electroconversion CO<sub>2</sub>‐to‐Formate by Oxygen‐Vacancy‐Rich Ultrasmall Bi‐Based Catalyst Over a Wide Potential Window

2022· article· en· W4205134537 on OpenAlexaff
Xueli Wang, Lu‐Hua Zhang, Datong Chen, Jiayu Zhan, Jiangyi Guo, Zisheng Zhang, Fengshou Yu

Bibliographic record

VenueChemCatChem · 2022
Typearticle
Languageen
FieldEnergy
TopicCO2 Reduction Techniques and Catalysts
Canadian institutionsUniversity of Ottawa
FundersNatural Science Foundation of Hebei ProvinceNational Natural Science Foundation of China
KeywordsFormateSelectivityCatalysisMaterials scienceBismuthGrapheneElectrochemistryVacancy defectOxideOxygenInorganic chemistryNanotechnologyChemical engineeringChemistryElectrodePhysical chemistryOrganic chemistryCrystallography

Abstract

fetched live from OpenAlex

Abstract Bi‐based materials quickly rise as promising candidates for electrochemical CO 2 ‐to‐formate conversion. However, most of them only display a narrow potential range for the desired formate selectivity. Herein, we designed an oxygen‐vacancy‐rich ultrasmall bismuth subcarbonate supported on reduced graphene oxide composite (Vo‐BOC/G) for electrochemical CO 2 ‐to‐formate conversion. The Vo‐BOC/G exhibits an outstanding formate selectivity up to 100 % at −1.2 V vs. RHE and an impressive partial current density of 38 mA cm −2 in 0.1 M KHCO 3 . More importantly, the considerable formate selectivity (&gt;80 %) was obtained over an impressively wide potential range of 600 mV, superior over most of reported Bi‐based electrocatalysts under the same conditions. Theoretical results show the abundant Vo defects significantly lower energy barrier for *CO 2 formation, resulting in high formate selectivity over a wider potential window. This work may pave the way for Bi‐based materials application in different renewable energy‐conversion devices.

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), Insufficient payload (model declined to judge)
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 score0.011
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
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.005
GPT teacher head0.210
Teacher spread0.205 · 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
Published2022
Admission routes1
Has abstractyes

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