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Enhanced photodetection properties of CVD-grown MOS2 nanosheets onto Ag-nanoparticles decorated substrates

2023· article· en· W4385624324 on OpenAlexaff
Driss Mouloua, M. El Marssi, My Alı El Khakani, Mustapha Jouiad

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
Topic2D Materials and Applications
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsMaterials sciencePhotodetectionNanoparticleChemical vapor depositionNanotechnologyNanostructurePulsed laser depositionDeposition (geology)OptoelectronicsChemical engineeringThin filmPhotodetector

Abstract

fetched live from OpenAlex

Coupling two-dimensional (2D)-MoS2 nanostructures with metal nanoparticles (NPs) can lead to unprecedented behaviors because of the coupling between the excitons in Mos2and the plasmons of the metal NPs. Here, we report the chemical vapor deposition (CVD) growth of vertical 2D-MoS2 nanosheets onto quartz substrates pre-decorated by Ag-NPs. The Ag-NPs were first deposited by pulsed laser deposition (PLD) and used as a catalyst in order to gain more control on the morphology of Mos2.By depositing Mos2on the PLD-deposited Ag-NPs, we found that the morphology of the CVD grown Mos2changes from planar to vertical 2D- nanosheets. Moreover, the Ag- NPs were also shown to favor the growth of high-quality 2H-MoS2 phase. Our preliminary results show that the light absorption of these novel Ag-NPs/MoS2 composite films can be enhanced over the entire visible spectrum, leading thereby to an improvement of their specific detectivity (D*) by as high as 516%. This work paves the way towards to design and develop highly responsive optoelectronic devices based on the optimized combination of metal-NPs and 2D-MoS2 nanostructures.

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 score0.002
Threshold uncertainty score1.000

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)0.0010.001

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 head0.032
GPT teacher head0.248
Teacher spread0.216 · 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

Citations3
Published2023
Admission routes1
Has abstractyes

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