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Record W4291020063 · doi:10.1002/adma.202204637

Dual‐Scale Integration Design of Sn–ZnO Catalyst toward Efficient and Stable CO<sub>2</sub> Electroreduction

2022· article· en· W4291020063 on OpenAlexafffund
Bohua Ren, Zhen Zhang, Guobin Wen, Xiaowen Zhang, Mi Xu, Yueying Weng, Yihang Nie, Haozhen Dou, Yi Jiang, Ya‐Ping Deng, Guiru Sun, Dan Luo, Lingling Shui, Xin Wang, Ming Feng, Aiping Yu, Zhongwei Chen

Bibliographic record

VenueAdvanced Materials · 2022
Typearticle
Languageen
FieldEnergy
TopicCO2 Reduction Techniques and Catalysts
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaGuangdong Science and Technology DepartmentBasic and Applied Basic Research Foundation of Guangdong ProvinceUniversity of WaterlooChina Postdoctoral Science Foundation
KeywordsMaterials scienceNanoporeElectrochemistryCatalysisFaraday efficiencyNanotechnologyChemical engineeringNoble metalSubstrate (aquarium)Reversible hydrogen electrodeLeaching (pedology)ElectrodeMetalWorking electrodeMetallurgy

Abstract

fetched live from OpenAlex

emission and producing valuable fuels. In the quest to resolve its current problems of low-energy efficiency and insufficient durability, a dual-scale design strategy is proposed by implanting a non-noble active Sn-ZnO heterointerface inside the nanopores of high-surface-area carbon nanospheres (Sn-ZnO@HC). The metal d-bandwidth tuning of Sn and ZnO alters the extent of substrate-molecule orbital mixing, facilitating the breaking of the *COOH intermediate and the yield of CO. Furthermore, the confinement effect of tailored nanopores results in a beneficial pH distribution in the local environment around the Sn-ZnO nanoparticles and protects them against leaching and aggregating. Through integrating electronic and nanopore-scale control, Sn-ZnO@HC achieves a quite low potential of -0.53 V vs reversible hydrogen electrode (RHE) with 91% Faradaic efficiency for CO and an ultralong stability of 240 h. This work provides proof of concept for the multiscale design of electrocatalysts.

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 score0.017
Threshold uncertainty score0.676

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 head0.012
GPT teacher head0.241
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

Citations67
Published2022
Admission routes2
Has abstractyes

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