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Record W748825420 · doi:10.1038/ncomms8664

The surface structure of silver-coated gold nanocrystals and its influence on shape control

2015· article· en· W748825420 on OpenAlexafffund
J. Daniel Padmos, Michelle L. Personick, Qing Tang, Paul N. Duchesne, De‐en Jiang, Chad A. Mirkin, Peng Zhang

Bibliographic record

VenueNature Communications · 2015
Typearticle
Languageen
FieldMaterials Science
TopicGold and Silver Nanoparticles Synthesis and Applications
Canadian institutionsDalhousie University
FundersDivision of Materials ResearchUniversity of California, RiversideOffice of ScienceMaterials Research Science and Engineering Center, Harvard UniversityNational Energy Research Scientific Computing CenterNorthwestern UniversityUniversity of SaskatchewanBasic Energy SciencesNatural Sciences and Engineering Research Council of CanadaCanadian Light SourceCanadian Institutes of Health ResearchNational Science FoundationUniversity of WashingtonArgonne National LaboratoryU.S. Department of DefenseU.S. Department of Energy
KeywordsNanocrystalMaterials scienceNanotechnologySurface structureSurface (topology)CrystallographyChemistryMathematicsGeometry

Abstract

fetched live from OpenAlex

Understanding the surface structure of metal nanocrystals with specific facet indices is important due to its impact on controlling nanocrystal shape and functionality. However, this is particularly challenging for halide-adsorbed nanocrystals due to the difficulty in analysing interactions between metals and light halides (for example, chloride). Here we uncover the surface structures of chloride-adsorbed, silver-coated gold nanocrystals with {111}, {110}, {310} and {720} indexed facets by X-ray absorption spectroscopy and density functional theory modelling. The silver-chloride, silver-silver and silver-gold bonding structures are markedly different between the nanocrystal surfaces, and are sensitive to their formation mechanism and facet type. A unique approach of combining the density functional theory and experimental/simulated X-ray spectroscopy further verifies the surface structure models and identifies the previously indistinguishable valence state of silver atoms on the nanocrystal surfaces. Overall, this work elucidates the thus-far unknown chloride-metal nanocrystal surface structures and sheds light onto the halide-induced growth mechanism of anisotropic nanocrystals.

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 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 score0.031
Threshold uncertainty score0.279

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 communication0.0000.000
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 head0.021
GPT teacher head0.274
Teacher spread0.254 · 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

Citations60
Published2015
Admission routes2
Has abstractyes

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