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Record W4389308488 · doi:10.1002/adma.202310822

Cu‐In Dual Sites with Sulfur Defects toward Superior Ethanol Electrosynthesis from CO<sub>2</sub> Electrolysis

2023· article· en· W4389308488 on OpenAlexafffund
Guobin Wen, Bohua Ren, Xiaowen Zhang, Shuxuan Liu, Li Xu, Lu Han, Yuanmei Xu, Eser Metin Akinoglu, Tao Li, Dan Luo, Qianyi Ma, Xin Wang, Renfei Feng, Shuangyin Wang, Aiping Yu, Zhongwei Chen

Bibliographic record

VenueAdvanced Materials · 2023
Typearticle
Languageen
FieldEnergy
TopicCO2 Reduction Techniques and Catalysts
Canadian institutionsCanadian Light Source (Canada)University of Waterloo
FundersHunan UniversityUniversity of WaterlooNational Supercomputer Centre in GuangzhouBasic and Applied Basic Research Foundation of Guangdong ProvinceChinese Academy of SciencesCanadian Light Source
KeywordsElectrosynthesisMaterials scienceCatalysisSulfurCarbon fibersElectrolysisIntercalation (chemistry)Yield (engineering)Chemical engineeringInorganic chemistryElectrochemistryPhysical chemistryChemistryOrganic chemistryElectrolyteElectrode

Abstract

fetched live from OpenAlex

Abstract The electrosynthesis of multi‐carbon chemicals from excess carbon dioxide (CO 2 ) is an area of great interest for research and commercial applications. However, improving both the yield of CO 2 ‐to‐ethanol conversion and the stability of the catalyst at the same time is proving to be a challenging issue. Here it is proposed to stabilize active Cu(I) and In dual sites with sulfur defects through an electro‐driven intercalation strategy, which leads to the delocalization of electron density that enhances orbital hybridizations between the Cu‐C and In‐H bonds. Hence, the energy barrier for the rate‐limiting *CHO formation step is reduced toward the key *OCHCHO* formation during ethanol production, which is also facilitated by the combined Cu site enabling C‐C coupling and In site with a higher oxygen affinity based on both thermodynamic and kinetic calculations. Accordingly, such dual‐site catalyst achieves a high partial current density toward ethanol of 409 ± 15 mA cm⁻ 2 for over 120 h. Furthermore, a scaled‐up flow cell is assembled with an industrial‐relevant current of 5.7 A for over 36 h, in which the carbon loss is less than 2.5% and single‐pass carbon efficiency is ≈19%.

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)
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 score0.005
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.011
GPT teacher head0.242
Teacher spread0.231 · 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

Citations22
Published2023
Admission routes2
Has abstractyes

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