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Record W4401752852 · doi:10.1073/pnas.2408183121

A synergetic cocatalyst for conversion of carbon dioxide, sunlight, and water into methanol

2024· article· en· W4401752852 on OpenAlexafffund
Zhengwei Ye, Ke Yang, Bingxing Zhang, Ishtiaque Ahmed Navid, Yifan Shen, Yixin Xiao, Alexandre Pofelski, Gianluigi A. Botton, Tao Ma, Shubham Mondal, Theodore B. Norris, Víctor S. Batista, Zetian Mi

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2024
Typearticle
Languageen
FieldEnergy
TopicAdvanced Photocatalysis Techniques
Canadian institutionsMcMaster University
FundersArmy Research OfficeNatural Sciences and Engineering Research Council of CanadaMcMaster University
KeywordsPhotocatalysisHydrogenMethanolMaterials scienceElectrochemical reduction of carbon dioxideChemical engineeringCarbon fibersCatalysisHydrogen productionElectron transferPhotochemistryChemistryNanotechnologyCarbon monoxideOrganic chemistry

Abstract

fetched live from OpenAlex

The conversion of CO 2 into liquid fuels, using only sunlight and water, offers a promising path to carbon neutrality. An outstanding challenge is to achieve high efficiency and product selectivity. Here, we introduce a wireless photocatalytic architecture for conversion of CO 2 and water into methanol and oxygen. The catalytic material consists of semiconducting nanowires decorated with core–shell nanoparticles, with a copper-rhodium core and a chromium oxide shell. The Rh/CrOOH interface provides a unidirectional channel for proton reduction, enabling hydrogen spillover at the core–shell interface. The vectorial transfer of protons, electrons, and hydrogen atoms allows for switching the mechanism of CO 2 reduction from a proton-coupled electron transfer pathway in aqueous solution to hydrogenation of CO 2 with a solar-to-methanol efficiency of 0.22%. The reported findings demonstrate a highly efficient, stable, and scalable wireless system for synthesis of methanol from CO 2 that could provide a viable path toward carbon neutrality and environmental sustainability.

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)0.001
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 score0.050
Threshold uncertainty score0.266

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.022
GPT teacher head0.304
Teacher spread0.282 · 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

Citations15
Published2024
Admission routes2
Has abstractyes

Explore more

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