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Record W4401397289 · doi:10.1002/smll.202405727

Ag<sub>20</sub> Nanoclusters with Surface Azides as an Easily Functionalized Platform for Diverse Chemical Applications

2024· article· en· W4401397289 on OpenAlexafffund
Alexander H. Stöckli, Johanna A. de Jong, Carolina Vega Verduga, Nils H. Vogeler, Mahdi Hesari, Paul D. Boyle, John F. Corrigan, Mark S. Workentin

Bibliographic record

VenueSmall · 2024
Typearticle
Languageen
FieldMaterials Science
TopicNanocluster Synthesis and Applications
Canadian institutionsUniversity of WaterlooWestern University
FundersNatural Sciences and Engineering Research Council of CanadaMcMaster University
KeywordsNanoclustersNanotechnologyMaterials scienceSurface modificationChemical engineeringEngineering

Abstract

fetched live from OpenAlex

Abstract Modifying atomically precise nanocluster surfaces while maintaining the cluster core remains a key challenge. Herein, the synthesis, structure, and properties of two targeted Ag 20 nanoclusters (NCs) with eight surface azide moieties, [CO 3 @Ag 20 (S t Bu) 10 ( m ‐N 3 ‐C 6 H 4 COO) 8 (DMF) 4 ] ( 1 ‐m ) and [CO 3 @Ag 20 (S t Bu) 10 ( p ‐N 3 ‐C 6 H 4 COO) 8 (DMF) 4 ] ( 1 ‐p ) are reported, where DMF is N,N ‐dimethylformamide. These AgNCs are designed to undergo cluster surface strain‐promoted azide‐alkyne cycloaddition (CS‐SPAAC) reactions, introducing new functionality to the cluster surface. Reactivity is screened using model strained cyclooctynes. Reaction products and parent clusters are characterized by UV–vis, FT‐IR, and NMR spectroscopies. The structure of the parent clusters and presence of surface azides is confirmed by single crystal X‐ray diffraction (SCXRD) analysis. Clusters 1 ‐m and 1 ‐p are found to be amenable to CS‐SPAAC reactions with retention of the NC frameworks, opening new routes for efficient modification of AgNC for applications.

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 score0.086
Threshold uncertainty score0.666

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.027
GPT teacher head0.254
Teacher spread0.227 · 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

Citations4
Published2024
Admission routes2
Has abstractyes

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