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Record W4323040022 · doi:10.3389/fchem.2023.1132567

PVD growth of spiral pyramid-shaped WS2 on SiO2/Si driven by screw dislocations

2023· article· en· W4323040022 on OpenAlexaff
Yassine Madoune, DingBang Yang, Yameen Ahmed, Mansour M. Al-Makeen, Han Huang

Bibliographic record

VenueFrontiers in Chemistry · 2023
Typearticle
Languageen
FieldMaterials Science
Topic2D Materials and Applications
Canadian institutionsUniversity of Victoria
FundersState Key Laboratory of Powder MetallurgyCentral South UniversityNational Natural Science Foundation of China
KeywordsPyramid (geometry)Spiral (railway)Materials scienceCrystallographyNanotechnologyChemistryOpticsPhysicsMechanical engineeringEngineering

Abstract

fetched live from OpenAlex

Atomically thin layered transition metal dichalcogenides (TMDs), such as MoS 2 and WS 2 , have been getting much attention recently due to their interesting electronic and optoelectronic properties. Especially, spiral TMDs provide a variety of candidates for examining the light-matter interaction resulting from the broken inversion symmetry, as well as the potential new utilization in functional optoelectronic, electromagnetic and nanoelectronics devices. To realize their potential device applications, it is desirable to achieve controlled growth of these layered nanomaterials with a tunable stacking. Here, we demonstrate the Physical Vapor Deposition (PVD) growth of spiral pyramid-shaped WS 2 with ∼200 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m1"><mml:mrow><mml:mi>μ</mml:mi><mml:mi>m</mml:mi></mml:mrow></mml:math> in size and the interesting optical properties via AFM and Raman spectroscopy. By controlling the precursors concentration and changing the initial nucleation rates in PVD growth, WS 2 in different nanoarchitectures can be obtained. We discuss the growth mechanism for these spiral-patterned WS 2 nanostructures based on the screw dislocations. This study provides a simple, scalable approach of screw dislocation-driven (SDD) growth of distinct TMD nanostructures with varying morphologies, and stacking.

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 score0.039
Threshold uncertainty score0.561

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)0.0000.000

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 head0.010
GPT teacher head0.241
Teacher spread0.230 · 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

Citations11
Published2023
Admission routes1
Has abstractyes

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