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Record W2981613025 · doi:10.1038/s41467-019-12788-0

Dopant-tuned stabilization of intermediates promotes electrosynthesis of valuable C3 products

2019· article· en· W2981613025 on OpenAlexafffund
Tao‐Tao Zhuang, Dae‐Hyun Nam, Ziyun Wang, Hui‐Hui Li, Christine M. Gabardo, Yi Li, Zhiqin Liang, Jun Li, Xiaojing Liu, Bin Chen, Wan Ru Leow, Rui Wu, Xue Wang, Fengwang Li, Yanwei Lum, Joshua Wicks, Colin P. O’Brien, Tao Peng, Alexander H. Ip, Tsun‐Kong Sham, Shu‐Hong Yu, David Sinton, Edward H. Sargent

Bibliographic record

VenueNature Communications · 2019
Typearticle
Languageen
FieldEnergy
TopicCO2 Reduction Techniques and Catalysts
Canadian institutionsWestern UniversityUniversity of Toronto
FundersOffice of ScienceMitacsTianjin UniversityUniversity of TorontoFoundation for Innovative Research Groups of the National Natural Science Foundation of ChinaGovernment of OntarioNational Natural Science Foundation of ChinaTianjin University of TechnologyNatural Sciences and Engineering Research Council of CanadaCanadian Light SourceCanadian Institute for Advanced ResearchOntario Centres of ExcellenceArgonne National LaboratoryU.S. Department of Energy
KeywordsElectrosynthesisFaraday efficiencyElectrocatalystPalladiumAnodeDopantElectrochemistryMaterials scienceMethanolChemistryCatalysisChemical engineeringCombinatorial chemistryDopingElectrodeOrganic chemistryPhysical chemistry

Abstract

fetched live from OpenAlex

/CO feedstocks to higher-value chemicals via energy-efficient electrochemical processes enables carbon utilization and renewable energy storage. Substantial progress has been made to improve performance at the cathodic side; whereas less progress has been made on improving anodic electro-oxidation reactions to generate value. Here we report the efficient electroproduction of value-added multi-carbon dimethyl carbonate (DMC) from CO and methanol via oxidative carbonylation. We find that, compared to pure palladium controls, boron-doped palladium (Pd-B) tunes the binding strength of intermediates along this reaction pathway and favors DMC formation. We implement this doping strategy and report the selective electrosynthesis of DMC experimentally. We achieve a DMC Faradaic efficiency of 83 ± 5%, fully a 3x increase in performance compared to the corresponding pure Pd electrocatalyst.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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)0.0020.000

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 head0.273
Teacher spread0.260 · 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 source (direct Gemma or distilled Codex), 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

Citations38
Published2019
Admission routes2
Has abstractyes

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Same venueNature CommunicationsSame topicCO2 Reduction Techniques and CatalystsFrench-language works237,207