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Record W3022715573 · doi:10.1002/anie.202004149

Ternary Sn‐Ti‐O Electrocatalyst Boosts the Stability and Energy Efficiency of CO<sub>2</sub> Reduction

2020· article· en· W3022715573 on OpenAlexafffund
Guobin Wen, Bohua Ren, Moon Gyu Park, Jie Yang, Haozhen Dou, Zhen Zhang, Ya‐Ping Deng, Zhengyu Bai, Lin Yang, Jeff T. Gostick, Gianluigi A. Botton, Yongfeng Hu, Zhongwei Chen

Bibliographic record

VenueAngewandte Chemie International Edition · 2020
Typearticle
Languageen
FieldEnergy
TopicCO2 Reduction Techniques and Catalysts
Canadian institutionsCanadian Light Source (Canada)University of SaskatchewanMcMaster UniversityUniversity of Waterloo
FundersHenan UniversityNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of ChinaHenan Normal University
KeywordsElectrocatalystTernary operationReduction (mathematics)Materials scienceChemical engineeringChemistryPhysical chemistryComputer scienceElectrochemistryElectrodeMathematicsEngineering

Abstract

fetched live from OpenAlex

Abstract Simultaneously improving energy efficiency (EE) and material stability in electrochemical CO 2 conversion remains an unsolved challenge. Among a series of ternary Sn‐Ti‐O electrocatalysts, 3D ordered mesoporous (3DOM) Sn 0.3 Ti 0.7 O 2 achieves a trade‐off between active‐site exposure and structural stability, demonstrating up to 71.5 % half‐cell EE over 200 hours, and a 94.5 % Faradaic efficiency for CO at an overpotential as low as 430 mV. DFT and X‐ray absorption fine structure analyses reveal an electron density reconfiguration in the Sn‐Ti‐O system. A downshift of the orbital band center of Sn and a charge depletion of Ti collectively facilitate the dissociative adsorption of the desired intermediate COOH* for CO formation. It is also beneficial in maintaining a local alkaline environment to suppress H 2 and formate formation, and in stabilizing oxygen atoms to prolong durability. These findings provide a new strategy in materials design for efficient CO 2 conversion and beyond.

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 score0.032
Threshold uncertainty score0.542

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 head0.013
GPT teacher head0.236
Teacher spread0.223 · 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

Citations95
Published2020
Admission routes2
Has abstractyes

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