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Record W2323211075 · doi:10.1021/jp302336k

One-Step, Cooperative Deposition of Au–Ni Alloy Nanoparticles on a Si Substrate: “Catalytic” Growth Mechanism in an Aqueous Medium

2012· article· en· W2323211075 on OpenAlexaff
Liyan Zhao, Nina F. Heinig, K. T. Leung

Bibliographic record

VenueThe Journal of Physical Chemistry C · 2012
Typearticle
Languageen
FieldMaterials Science
TopicCatalytic Processes in Materials Science
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsAqueous mediumAlloyNanoparticleSubstrate (aquarium)CatalysisDeposition (geology)Chemical engineeringMaterials scienceMechanism (biology)Aqueous solutionNanotechnologyChemistryMetallurgyPhysical chemistryOrganic chemistryGeologyEngineering

Abstract

fetched live from OpenAlex

Gold–nickel alloy nanoparticles are prepared on a H-terminated Si(100) substrate by a simple, one-step cooperative electrochemical deposition process at room temperature. The spherical alloy nanoparticles, with an average diameter of 6–12 nm, are obtained by amperometry at −0.8 V (with respect to a Ag/AgCl reference electrode) for 2–10 s in an aqueous solution of 0.05 mM AuCl 3, 5 mM NiCl 2, and 100 mM NaClO 4 . Depth-profiling X-ray photoelectron spectroscopy reveals interesting evolution in Au 4f or/and Ni 2p binding energy shifts with respect to sputtering depth, which supports the formation of a Au–Ni alloy during the codeposition via a new “catalytic” growth mechanism. In particular, Au nanocrystallites that are deposited first onto the substrate act as a catalyst to initiate the subsequent Ni deposition. Codeposition of Au and Ni then occurs simultaneously, and the presence of Au and Ni nanodeposits further promotes the growth of each other, producing alloy nanoparticles. The glancing-incidence X-ray diffraction pattern of the new Au–Ni nanoparticles exhibits a small shift to larger 2θ, corresponding to smaller lattice parameters, than pristine Au nanoparticles, confirming the alloy formation. The Au–Ni alloy formation by coelectrochemical deposition promises potential applications in designing new catalysts.

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 score0.001
Threshold uncertainty score0.472

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 studies0.0000.000
Scholarly communication0.0000.001
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 head0.017
GPT teacher head0.264
Teacher spread0.247 · 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

Citations12
Published2012
Admission routes1
Has abstractyes

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