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Record W3134915049 · doi:10.1038/s41467-021-21901-1

Double sulfur vacancies by lithium tuning enhance CO2 electroreduction to n-propanol

2021· article· en· W3134915049 on OpenAlexafffund
Peng Chen, Gan Luo, Junbo Zhang, Menghuan Chen, Zhiqiang Wang, Tsun‐Kong Sham, Lijuan Zhang, Yafei Li, Gengfeng Zheng

Bibliographic record

VenueNature Communications · 2021
Typearticle
Languageen
FieldEnergy
TopicCO2 Reduction Techniques and Catalysts
Canadian institutionsWestern University
FundersArgonne National LaboratoryNational Key Research and Development Program of ChinaOffice of ScienceU.S. Department of EnergyScience and Technology Commission of Shanghai MunicipalityShanghai Municipal Education CommissionNational Natural Science Foundation of ChinaCanadian Light Source
KeywordsElectrochemistrySulfurVacancy defectFaraday efficiencyCatalysisMaterials scienceLithium (medication)Density functional theoryElectrodeInorganic chemistryChemistryCrystallographyPhysical chemistryComputational chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Electrochemical CO 2 reduction can produce valuable products with high energy densities but the process is plagued by poor selectivities and low yields. Propanol represents a challenging product to obtain due to the complicated C 3 forming mechanism that requires both stabilization of *C 2 intermediates and subsequent C 1 –C 2 coupling. Herein, density function theory calculations revealed that double sulfur vacancies formed on hexagonal copper sulfide can feature as efficient electrocatalytic centers for stabilizing both CO* and OCCO* dimer, and further CO–OCCO coupling to form C 3 species, which cannot be realized on CuS with single or no sulfur vacancies. The double sulfur vacancies were then experimentally synthesized by an electrochemical lithium tuning strategy, during which the density of sulfur vacancies was well-tuned by the charge/discharge cycle number. The double sulfur vacancy-rich CuS catalyst exhibited a Faradaic efficiency toward n-propanol of 15.4 ± 1% at −1.05 V versus reversible hydrogen electrode in H-cells, and a high partial current density of 9.9 mA cm −2 at −0.85 V in flow-cells, comparable to the best reported electrochemical CO 2 reduction toward n-propanol. Our work suggests an attractive approach to create anion vacancy pairs as catalytic centers for multi-carbon-products.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.529
Threshold uncertainty score0.697

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)0.0000.000

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 head0.015
GPT teacher head0.301
Teacher spread0.287 · 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

Citations347
Published2021
Admission routes2
Has abstractyes

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