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Record W2749155564 · doi:10.1139/cjc-2017-0169

Bonding properties of FCC-like Au<sub>44</sub>(SR)<sub>28</sub> clusters from X-ray absorption spectroscopy

2017· article· en· W2749155564 on OpenAlexaffvenue
Rui Yang, Daniel M. Chevrier, Chenjie Zeng, Rongchao Jin, Peng Zhang

Bibliographic record

VenueCanadian Journal of Chemistry · 2017
Typearticle
Languageen
FieldMaterials Science
TopicNanocluster Synthesis and Applications
Canadian institutionsDalhousie University
Fundersnot available
KeywordsX-ray absorption spectroscopyExtended X-ray absorption fine structureChemistryAbsorption spectroscopySpectroscopyCrystallographyAbsorption (acoustics)MetalCluster (spacecraft)Transition metalAnalytical Chemistry (journal)CatalysisMaterials science

Abstract

fetched live from OpenAlex

Thiolate-protected gold clusters with precisely controlled atomic composition have recently emerged as promising candidates for a variety of applications because of their unique optical, electronic, and catalytic properties. The recent discovery of the Au 44 (SR) 28 total structure is considered as an interesting finding in terms of the face-centered cubic (FCC)-like core structure in small gold-thiolate clusters. Herein, the unique bonding properties of Au 44 (SR) 28 is analyzed using temperature-dependent X-ray absorption spectroscopy (XAS) measurements at the Au L 3 -edge and compared with other FCC-like clusters such as Au 36 (SR) 24 and Au 28 (SR) 20 . A negative thermal expansion was detected for the Au–Au bonds of the metal core (the first Au–Au shell) and was interpreted based on the unique Au core structure consisting of the Au 4 units. EXAFS fitting results from Au 28 (SR) 20 , Au 36 (SR) 24 , and Au 44 (SR) 28 show a size-dependent negative thermal expansion behavior in the first Au–Au shell, further highlighting the importance of the Au 4 units in determining the Au core bonding properties and shedding light on the growth mechanism of these FCC-like Au clusters.

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 score0.002
Threshold uncertainty score0.785

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 head0.017
GPT teacher head0.217
Teacher spread0.200 · 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

Citations8
Published2017
Admission routes2
Has abstractyes

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