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Record W2742232894 · doi:10.1116/1.4996550

Low-temperature and scalable CVD route to WS2 monolayers on SiO2/Si substrates

2017· article· en· W2742232894 on OpenAlexfundno aff
Stéphane Cadot, O. Renault, D. Rouchon, D. Mariolle, Emmanuel Nolot, Chloé Thieuleux, Laurent Veyre, Hanako Okuno, F. Martín, Elsje Alessandra Quadrelli

Bibliographic record

VenueJournal of Vacuum Science & Technology A Vacuum Surfaces and Films · 2017
Typearticle
Languageen
FieldMaterials Science
Topic2D Materials and Applications
Canadian institutionsnot available
FundersIndigenous and Northern Affairs CanadaCommissariat à l'Énergie Atomique et aux Énergies AlternativesCentre National de la Recherche Scientifique
KeywordsMonolayerMaterials scienceAmorphous solidAnnealing (glass)Tungsten disulfideChemical vapor depositionRaman spectroscopyTungstenChemical engineeringTransmission electron microscopyFabricationNanotechnologyX-ray photoelectron spectroscopyAnalytical Chemistry (journal)ChemistryCrystallographyMetallurgyOpticsOrganic chemistry

Abstract

fetched live from OpenAlex

Tungsten disulfide (WS2) monolayers are promising for next-generation flat electronics, but few scalable deposition methods are currently available. Here, the authors report the fabrication of tungsten disulfide monolayers through a novel two-step chemical vapor deposition process involving the deposition of an amorphous tungsten sulfide layer at a relatively mild temperature from the W(CO)6 and 1,2-ethanedithiol precursors, followed by a short annealing at 800 °C under an inert atmosphere. This two-step process allows the fabrication of a crystalline WS2 deposit with a low thermal budget. Raman, x-ray photoelectron, and wavelength dispersive x-ray fluorescence spectroscopic studies performed before and after annealing confirmed the deposition of a sulfur-rich amorphous intermediate, and further confirmed its conversion upon annealing toward oriented 2D WS2 crystals in the 1–2 monolayer range, as corroborated by high-resolution transmission electron microscopy.

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 categoriesScience and technology studies, Scholarly communication
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 score0.024
Threshold uncertainty score1.000

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 studies0.0020.001
Scholarly communication0.0010.001
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 head0.011
GPT teacher head0.267
Teacher spread0.256 · 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.

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

Citations18
Published2017
Admission routes1
Has abstractyes

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