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Record W2398733826 · doi:10.1021/acs.nanolett.5b01196

Probing the Dynamics of the Metallic-to-Semiconducting Structural Phase Transformation in MoS<sub>2</sub> Crystals

2015· article· en· W2398733826 on OpenAlexafffund
Yinsheng Guo, Dezheng Sun, Bin Ouyang, Archana Raja, Jun Song, Tony F. Heinz, Louis E. Brus

Bibliographic record

VenueNano Letters · 2015
Typearticle
Languageen
FieldMaterials Science
Topic2D Materials and Applications
Canadian institutionsMcGill University
FundersBasic Energy SciencesDivision of Materials ResearchSLAC National Accelerator LaboratoryNatural Sciences and Engineering Research Council of Canada
KeywordsMetastabilityNucleationPhase (matter)Materials scienceActivation energyFemtosecondRaman spectroscopyChemical physicsIntercalation (chemistry)CrystallographyLaserThermodynamicsPhysical chemistryChemistryOpticsInorganic chemistryPhysics

Abstract

fetched live from OpenAlex

We have investigated the phase transformation of bulk MoS2 crystals from the metastable metallic 1T/1T' phase to the thermodynamically stable semiconducting 2H phase. The metastable 1T/1T' material was prepared by Li intercalation and deintercalation. The thermally driven kinetics of the phase transformation were studied with in situ Raman and optical reflection spectroscopies and yield an activation energy of 400 ± 60 meV (38 ± 6 kJ/mol). We calculate the expected minimum energy pathways for these transformations using DFT methods. The experimental activation energy corresponds approximately to the theoretical barrier for a single formula unit, suggesting that nucleation of the phase transformation is quite local. We also report that femtosecond laser writing converts 1T/1T' to 2H in a single laser pass. The mechanisms for the phase transformation are discussed.

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 score0.005

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 head0.265
Teacher spread0.240 · 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

Citations222
Published2015
Admission routes2
Has abstractyes

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