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Record W4287446994 · doi:10.1021/acs.jpcc.2c04482

New Insights into the Bonding Properties of [Ag <sub>25</sub> (SR) <sub>18</sub> ] <sup>−</sup> Nanoclusters from X-ray Absorption Spectroscopy

2022· article· en· W4287446994 on OpenAlexafffund
Ziyi Chen, Andrew G. Walsh, Xiao Wei, Manzhou Zhu, Peng Zhang

Bibliographic record

VenueThe Journal of Physical Chemistry C · 2022
Typearticle
Languageen
FieldMaterials Science
TopicNanocluster Synthesis and Applications
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of CanadaDalhousie University
KeywordsNanoclustersExtended X-ray absorption fine structureMaterials scienceAbsorption (acoustics)MetalBond lengthCrystallographyAbsorption spectroscopySpectroscopyCoordination numberNanotechnologyChemistryCrystal structurePhysicsIonOpticsMetallurgy

Abstract

fetched live from OpenAlex

Atomically precise metal nanoclusters have attracted significant interest due to their molecule-like properties. [Ag25(SR)18]− is one of the Ag nanoclusters having a unique structure similar to its Au counterpart but different from most other Ag nanoclusters. In this study, a new five-shell fitting method was developed to analyze the extended X-ray absorption fine structure (EXAFS) spectra of [Ag25(SR)18]− to provide more insights into its bonding properties. This new method was successfully applied to compare the bond lengths as the temperature changed from 300 to 90 K. Interestingly, the metal core of [Ag25(SR)18]− showed negative thermal expansion behavior that was not observed for Au25(SR)18. These unique bonding properties of [Ag25(SR)18]− could be related to the Ag4 tetrahedral units found in the metal core, which were absent in Au25(SR)18. These new findings about its bonding properties can provide a better understanding of the structure–property relationship of [Ag25(SR)18]−. This new EXAFS analysis method could be applied to gain insights into the bonding properties of other metal nanoclusters.

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 score0.005
Threshold uncertainty score0.623

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 studies0.0010.000
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 head0.013
GPT teacher head0.223
Teacher spread0.210 · 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

Citations5
Published2022
Admission routes2
Has abstractyes

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