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Record W4387326748 · doi:10.1021/acs.nanolett.3c01905

Homogeneous Vacancies-Enhanced Orbital Hybridization for Selective and Efficient CO<sub>2</sub>-to-CO Electrocatalysis

2023· article· en· W4387326748 on OpenAlexaff
Yuntong Qin, Guangming Zhan, Cun Tang, Di Yang, Xibo Wang, Jianhua Yang, Chengliang Mao, Zhentian Hao, Shuangyu Wang, Yixin Qin, Hongmei Li, Ke Chen, Min Liu, Jie Li

Bibliographic record

VenueNano Letters · 2023
Typearticle
Languageen
FieldEnergy
TopicCO2 Reduction Techniques and Catalysts
Canadian institutionsUniversity of Toronto
FundersHenan UniversityNational Natural Science Foundation of China
KeywordsFaraday efficiencyElectrocatalystElectrochemistryCatalysisHomogeneousMaterials scienceChemical physicsSelectivityNanotechnologyNanomaterialsElectrodeChemistryPhysical chemistryPhysicsThermodynamics

Abstract

fetched live from OpenAlex

Crafting vacancies offers an efficient route to upgrade the selectivity and productivity of nanomaterials for CO 2 electroreduction. However, defective nanoelectrocatalysts bear catalytically active vacancies mostly on their surface, with the rest of the interior atoms adiaphorous for CO 2 -to-product conversion. Herein, taking nanosilver as a prototype, we arouse the catalytic ability of internal atoms by creating homogeneous vacancies realized via electrochemical reconstruction of silver halides. The homogeneous vacancies-rich nanosilver, compared to the surface vacancies-dominated counterpart, features a more positive d-band center to trigger an intensified hybridization of the Ag_d orbital with the C_P orbital of the *COOH intermediate, leading to an accelerated CO 2 -to-CO transformation. These structural and electronic merits allow a large-area (9 cm –2 ) electrode to generate nearly pure CO with a CO/H 2 Faradaic efficiency ratio of 6932 at an applied current of 7.5 A. These findings highlight the potential of designing new-type defects in realizing the industrialization of electrocatalytic CO 2 reduction.

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 score0.033
Threshold uncertainty score0.886

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 science0.0000.000
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 head0.007
GPT teacher head0.236
Teacher spread0.229 · 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

Citations43
Published2023
Admission routes1
Has abstractyes

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