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Record W1983711072 · doi:10.1021/ja2028102

Kinetic Control and Thermodynamic Selection in the Synthesis of Atomically Precise Gold Nanoclusters

2011· article· en· W1983711072 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of the American Chemical Society · 2011
Typearticle
Languageen
FieldMaterials Science
TopicNanocluster Synthesis and Applications
Canadian institutionsDalhousie University
FundersCanadian Institutes of Health Research
KeywordsNanoclustersChemistryKinetic energyDispersityNanotechnologyAtom (system on chip)CrystallographyChemical engineeringChemical physicsMaterials sciencePolymer chemistryOrganic chemistryPhysics

Abstract

fetched live from OpenAlex

This work presents a combined approach of kinetic control and thermodynamic selection for the synthesis of monodisperse 19 gold atom nanoclusters protected by thiolate groups. The step of kinetic control allows the formation of a proper size distribution of initial size-mixed Au(n)(SR)(m) nanoclusters following the reduction of a gold precursor. Unlike the synthesis of Au(25)(SR)(18) nanoclusters, which involves rapid reduction of the gold precursor by NaBH(4) followed by size focusing, the synthesis of 19-atom nanoclusters requires slow reduction effected by a weaker reducing agent, borane-tert-butylamine complex. The initially formed mixture of nanoclusters then undergoes size convergence into a monodisperse product by means of a prolonged aging process. The nanocluster formula was determined to be Au(19)(SC(2)H(4)Ph)(13). This work demonstrates the importance of both kinetic control of the initial size distribution of nanoclusters prior to size focusing and subsequent thermodynamic selection of stable nanoclusters as the final product.

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.

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 score0.048
Threshold uncertainty score0.205

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 studies0.0000.000
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 head0.009
GPT teacher head0.217
Teacher spread0.208 · 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