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Record W2326760881 · doi:10.1021/am4008853

One-Pot Environmentally Friendly Approach toward Highly Catalytically Active Bimetal-Nanoparticle-Graphene Hybrids

2013· article· en· W2326760881 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

VenueACS Applied Materials & Interfaces · 2013
Typearticle
Languageen
FieldChemistry
TopicNanomaterials for catalytic reactions
Canadian institutionsWestern UniversityCanadian Light Source (Canada)University of Saskatchewan
Fundersnot available
KeywordsBimetalBimetallic stripGrapheneMaterials scienceCatalysisNanoparticleChemical engineeringEnvironmentally friendlyIonic bondingNanotechnologyMetalComposite materialIonMetallurgyOrganic chemistryChemistry

Abstract

fetched live from OpenAlex

A one-pot universal approach with simple metal sputtering onto room temperature ionic liquids has been developed to prepare bimetal-nanoparticle (NP)-graphene hybrids, and the process is environmentally friendly and completely free of additives and byproducts. The graphene-supported bimetallic NPs have an Ag-based core and an Au/Pd-rich shell, demonstrated by the scanning transmission electron microscopy. The X-ray absorption near-edge spectroscopy using synchrotron radiation reveals the occurrence of charge redistribution at both the Ag@Au and Ag@Pd core-shell interfaces. The as-prepared Ag@Au and Ag@Pd bimetal-NP-graphene hybrids are highly catalytically active for reduction of 4-nitrophenol, whose catalytic activity is superior to the corresponding monometallic hybrids. The catalytic superiority is ascribed to the electronic structure modification and morphological irregularity of the graphene-supported bimetallic NPs.

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)0.000
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 score0.018
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.003

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 head0.014
GPT teacher head0.213
Teacher spread0.199 · 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