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Record W4416182340 · doi:10.1139/cjc-2025-0172

Site-specific bonding analysis of Pt- and Pd-doped Au <sub>25</sub> (SR) <sub>18</sub> nanoclusters using EXAFS simulations

2025· article· en· W4416182340 on OpenAlexafffundvenue
L.H. Zhang, Ziyi Chen, Andrew Walsh, Peng Zhang

Bibliographic record

VenueCanadian Journal of Chemistry · 2025
Typearticle
Languageen
FieldMaterials Science
TopicNanocluster Synthesis and Applications
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsNanoclustersExtended X-ray absorption fine structureAtom (system on chip)MetalDopingCoordination numberAbsorption (acoustics)Alloy

Abstract

fetched live from OpenAlex

Thiolate-protected Au nanoclusters such as Au 25 (SR) 18 are receiving increasing attention for their unique structural and electronic properties, especially when modified via single-atom doping. In this study, we employed extended X-ray absorption fine structure (EXAFS) simulations to investigate the effects of substituting the central Au atom with Pd and Pt in Au 25 (SR) 18 , generating Au 24 Pd(SR) 18 and Au 24 Pt(SR) 18 nanoclusters. Site-specific EXAFS spectra for the central, surface, and staple atomic sites as well as the averaged overall spectrum based on these key sites were analyzed to assess changes in coordination environments and bonding characteristics. Our findings reveal that while the doped central atoms exhibit bulk-like bonding, the surface Au atoms show the most pronounced structural changes, significantly influencing the overall EXAFS features. In contrast, the staple sites are less affected by single-atom doping and retain the original molecule-like properties. These results highlight the sensitivity of EXAFS in single-atom doping investigations of metal nanoclusters and the importance of site-specific analysis for their potential applications, such as in designing single-atom alloy nanocluster 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)0.000
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 score0.003
Threshold uncertainty score0.841

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.020
GPT teacher head0.246
Teacher spread0.226 · 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

Citations0
Published2025
Admission routes3
Has abstractyes

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