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Record W4385949040 · doi:10.3390/cryst13081271

Raman Spectroscopy of 2D MoS2 Interacting with Metals

2023· article· en· W4385949040 on OpenAlexaff
Francesco Tumino, Paolo D’Agosta, Valeria Russo, Andrea Li Bassi, Carlo S. Casari

Bibliographic record

VenueCrystals · 2023
Typearticle
Languageen
FieldMaterials Science
Topic2D Materials and Applications
Canadian institutionsQueen's University
FundersHorizon 2020 Framework Programme
KeywordsRaman spectroscopyTransition metalSemiconductorMolybdenum disulfideMetalFabricationMaterials scienceNanotechnologySpectroscopyMolybdenumOptoelectronicsChemistryOpticsPhysicsMetallurgyCatalysis

Abstract

fetched live from OpenAlex

The research on molybdenum disulphide (MoS2) has progressed remarkably in the last decade, prompted by the increasing interest for this material as a potential candidate in future ultrathin optoelectronic devices. MoS2 is a layered semiconductor with a gap in the visible region, which can be exfoliated down to the mono-layer form. Since the discovery of the exceptional optoelectronic properties of 2D MoS2, Raman spectroscopy has been extensively used as a tool to characterize the structure and thickness of MoS2 films. Recent works on MoS2-metal interfaces have shown that Raman spectra are significantly affected by the interaction with metals. However, a complete understanding of how such interaction modifies the MoS2 vibrational properties is still lacking. Studying this subject with both experimental and theoretical methods will provide fundamental insight into the interface physics of MoS2-metal systems, which is crucial for the fabrication of metal contacts and for the development of metal-assisted synthesis methods. This review summarizes the main results concerning Raman spectroscopy studies of heterosystems between MoS2 and transition metals, providing both a basis and directions for future research.

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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
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 head0.022
GPT teacher head0.292
Teacher spread0.270 · 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

Citations44
Published2023
Admission routes1
Has abstractyes

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