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Record W3089192328 · doi:10.1039/d0nr03863f

Morphology-controlled MoS<sub>2</sub> by low-temperature atomic layer deposition

2020· article· en· W3089192328 on OpenAlexaff
Chengxu Shen, Muhammad Hamid Raza, Patrick Amsalem, Thorsten Schultz, Norbert Koch, Nicola Pinna

Bibliographic record

VenueNanoscale · 2020
Typearticle
Languageen
FieldMaterials Science
Topic2D Materials and Applications
Canadian institutionsAdler
FundersChina Scholarship CouncilDeutsche Forschungsgemeinschaft
KeywordsAtomic layer depositionMorphology (biology)Layer (electronics)Materials scienceDeposition (geology)NanotechnologyChemical engineeringGeology

Abstract

fetched live from OpenAlex

Two-dimensional (2D) transition metal dichalcogenides (TMDs) such as MoS2 are materials for multifarious applications such as sensing, catalysis, and energy storage. Due to their peculiar charge-transport properties, it is always desired to control their morphologies from vertical nanostructures to horizontal basal-plane oriented smooth layers. In this work, we established a low-temperature ALD process for MoS2 deposition using bis(t-butylimino)bis(dimethylamino)molybdenum(vi) and H2S precursors. The ALD reaction parameters, including reaction temperature and precursor pulse times, are systematically investigated and optimized. Polycrystalline MoS2 is conformally deposited on carbon nanotubes, Si-wafers, and glass substrates. Moreover, the morphologies of the deposited MoS2 films are tuned from smooth film to vertically grown flakes, and to nano-dots, by controlling the reaction parameters/conditions. It is noticed that our MoS2 nanostructures showed morphology-dependent optical and electrocatalytic properties, allowing us to choose the required morphology for a targeted application.

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 categoriesInsufficient payload (model declined to judge)
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 score0.029
Threshold uncertainty score0.999

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)0.0000.001

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 head0.008
GPT teacher head0.218
Teacher spread0.210 · 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

Citations19
Published2020
Admission routes1
Has abstractyes

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