MétaCan
Menu
Back to cohort
Record W4392105795 · doi:10.1002/adfm.202400798

Natural Sunlight‐Driven CO<sub>2</sub> Hydrogenation into Light Olefins at Ambient Pressure over Bifunctional Cu‐Promoted CoFe Alloy Catalyst

2024· article· en· W4392105795 on OpenAlexaff
Shangbo Ning, Junwei Wang, Xiuting Wu, Ling Li, Senlin Zhang, Shaohua Chen, Xiaohui Ren, Linjie Gao, Yuchen Hao, Cuncai Lv, Yaguang Li, Jinhua Ye

Bibliographic record

VenueAdvanced Functional Materials · 2024
Typearticle
Languageen
FieldEnergy
TopicCO2 Reduction Techniques and Catalysts
Canadian institutionsCarbon Engineering (Canada)
FundersFuzhou UniversityHebei UniversityNatural Science Foundation of Hebei ProvinceChina Postdoctoral Science FoundationNational Natural Science Foundation of China
KeywordsBifunctionalCatalysisMaterials scienceSelectivityAlloyChemical engineeringPhotochemistryOrganic chemistryChemistryMetallurgy

Abstract

fetched live from OpenAlex

Abstract The natural sunlight‐driven conversion of CO 2 into valuable C 2+ products is urgently being pursued at ambient pressure, yet it poses a substantial challenge. Herein, a bifunctional Cu‐promoted CoFe alloy catalyst is designed for the natural light‐driven CO 2 hydrogenation into light olefins. Under a weak solar‐irradiation intensity of 0.45 kW m −2 (0.45 sun), the optimal catalyst exhibits excellent activity and selectivity, with an impressive 73.7% selectivity for C 2+ hydrocarbons and an outstanding 56.5% selectivity for C 2‐4 olefins, which is the best catalyst for C 2+ hydrocarbons photosynthesis from natural sunlight to date. The bifunctional design of the catalyst combines the advantages of both metallic Cu and CoFe alloy components, providing a synergistic effect that enhances the CO 2 hydrogenation performance. The Cu promoter plays a crucial role in enhancing the adsorption of CO 2 and hydrogen spillover, while the CoFe alloy provides a stable coupling site of the C 1 intermediate for promoting the C 2‐4 olefins. This study provides new insights into the design of bifunctional catalysts for CO 2 hydrogenation and opens up new possibilities for sustainable production of light olefins from renewable resources.

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), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.213
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.007
GPT teacher head0.233
Teacher spread0.226 · 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; both teacher heads agree on what is shown here.

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

Citations42
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueAdvanced Functional MaterialsSame topicCO2 Reduction Techniques and CatalystsFrench-language works237,207