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Record W3027479287 · doi:10.1038/s41467-020-16227-3

Unique hole-accepting carbon-dots promoting selective carbon dioxide reduction nearly 100% to methanol by pure water

2020· article· en· W3027479287 on OpenAlexafffund
Yiou Wang, Xu Liu, Xiaoyu Han, Robert Godin, Jialu Chen, Wuzong Zhou, Chaoran Jiang, Jamie F. Thompson, Karatok Mustafa, Stephen A. Shevlin, James R. Durrant, Zhengxiao Guo, Junwang Tang

Bibliographic record

VenueNature Communications · 2020
Typearticle
Languageen
FieldMaterials Science
TopicCarbon and Quantum Dots Applications
Canadian institutionsUniversity of British Columbia, Okanagan Campus
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of CanadaLeverhulme TrustEngineering and Physical Sciences Research CouncilChina Scholarship CouncilResearch Councils UK
KeywordsMethanolCarbon dioxideElectrochemical reduction of carbon dioxideSolar fuelPhotochemistryCarbon fibersPhotocatalysisAdsorptionAbsorption (acoustics)Materials scienceChemistryCarbon nitrideSyngasCarbon monoxideCatalysisOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Solar-driven CO2 reduction by abundant water to alcohols can supply sustainable liquid fuels and alleviate global warming. However, the sluggish water oxidation reaction has been hardly reported to be efficient and selective in CO2 conversion due to fast charge recombination. Here, using transient absorption spectroscopy, we demonstrate that microwave-synthesised carbon-dots (mCD) possess unique hole-accepting nature, prolonging the electron lifetime (t50%) of carbon nitride (CN) by six folds, favouring a six-electron product. mCD-decorated CN stably produces stoichiometric oxygen and methanol from water and CO2 with nearly 100% selectivity to methanol and internal quantum efficiency of 2.1% in the visible region, further confirmed by isotopic labelling. Such mCD rapidly extracts holes from CN and prevents the surface adsorption of methanol, favourably oxidising water over methanol and enhancing the selective CO2 reduction to alcohols. This work provides a unique strategy for efficient and highly selective CO2 reduction by water to high-value chemicals.

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 score0.001
Threshold uncertainty score0.002

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)0.0010.000

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 head0.022
GPT teacher head0.294
Teacher spread0.272 · 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

Citations309
Published2020
Admission routes2
Has abstractyes

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