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Record W2318952054 · doi:10.1021/ja107817x

Synthesis and Characterization of Single-Layer Silver−Decanethiolate Lamellar Crystals

2011· article· en· W2318952054 on OpenAlexaff
Liang Hu, Lito P. de la Rama, Mikhail Y. Efremov, Yonathan Anahory, F. Schiettekatte, L. H. Allen

Bibliographic record

VenueJournal of the American Chemical Society · 2011
Typearticle
Languageen
FieldMaterials Science
TopicNanocluster Synthesis and Applications
Canadian institutionsRegroupement Québécois sur les Matériaux de Pointe
Fundersnot available
KeywordsLamellar structureTransmission electron microscopyChemistryFourier transform infrared spectroscopyAnnealing (glass)CrystallographyX-ray reflectivitySpectroscopyDiffractionLayer (electronics)X-ray crystallographyAnalytical Chemistry (journal)Thin filmMaterials scienceOpticsNanotechnologyComposite material

Abstract

fetched live from OpenAlex

We report the synthesis of silver-decanethiolate (AgSC10) lamellar crystals. Nanometer-sized Ag clusters grown on inert substrates react with decanethiol vapor to form multilayer AgSC10 lamellar crystals with both layer-by-layer and in-plane ordering. The crystals have strong (010) texture with the layers parallel to the substrates. The synthesis method allows for a precise control of the number of layers. The thickness of the lamellae can be manipulated and systematically reduced to a single layer by decreasing the amount of Ag and lowering the annealing temperature. The single-layer AgSC10 lamellae are two-dimensional crystals and have uniform thickness and in-plane ordering. These samples were characterized with nanocalorimetry, atomic force microscopy (AFM), transmission electron microscopy (TEM), X-ray diffraction (XRD), X-ray reflectivity (XRR), Fourier transform infrared spectroscopy (FTIR), and Rutherford backscattering spectroscopy (RBS).

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.024
GPT teacher head0.232
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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), 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

Citations55
Published2011
Admission routes1
Has abstractyes

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