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Record W4220911834 · doi:10.1016/j.isci.2022.104177

Synthetic strategies for MOF-based single-atom catalysts for photo- and electro-catalytic CO2 reduction

2022· review· en· W4220911834 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueiScience · 2022
Typereview
Languageen
FieldEnergy
TopicCO2 Reduction Techniques and Catalysts
Canadian institutionsUniversity of Toronto
FundersBasic and Applied Basic Research Foundation of Guangdong ProvinceNational Key Research and Development Program of ChinaTsinghua UniversityNational Natural Science Foundation of China
KeywordsCatalysisPhotocatalysisRenewable energyElectrochemical reduction of carbon dioxideNanotechnologyCarbon dioxideMaterials scienceSelectivityMetal-organic frameworkAtom (system on chip)PhotochemistryYield (engineering)ChemistryCombinatorial chemistryOrganic chemistryComputer scienceCarbon monoxideElectrical engineering

Abstract

fetched live from OpenAlex

reduction, driven by wind electricity and solar energy, are feasible ways of tackling carbon dioxide emission, as both energies are clean and renewable. Single-atom catalyst (SAC) is a candidate owing to excellent electrocatalytic and photocatalytic performance. Methods for preparing an SAC by using metal-organic frameworks (MOFs) as support or precursors are summarized. Also, applications in energy conversion are exhibited. However, the real challenge is to improve the selectivity of catalytic reactions to yield higher value products, which is to be discussed.

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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.990
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.055
GPT teacher head0.319
Teacher spread0.263 · 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