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Record W3158494163 · doi:10.1088/2053-1583/abf98c

Thin-suspended 2D materials: facile, versatile, and deterministic transfer assembly

2021· article· en· W3158494163 on OpenAlexafffund

Bibliographic record

Venue2D Materials · 2021
Typearticle
Languageen
FieldMaterials Science
Topic2D Materials and Applications
Canadian institutionsConcordia University
FundersNatural Sciences and Engineering Research Council of CanadaConcordia University
KeywordsSuspension (topology)FabricationGrapheneRaman scatteringNitrocelluloseRaman spectroscopyBilayer

Abstract

fetched live from OpenAlex

Abstract We report a deterministic 2D material (2DM) transfer method to assemble any-stacking-order heterostructures incorporating suspended ultra-thin 2DMs, such as single-layer graphene (SLG) and bilayer graphene (BLG). The transfer procedure relies on a single-step preparation nitrocellulose micro-stamp, which combines both outstanding adhesion and softness. It permits the dry pick-up of naked 2D crystals (graphene, MoS 2 , and hBN) directly from a SiO 2 substrate, and to precisely transfer them on substrates or trenches. Optical and Raman data show that no significant defect is introduced upon transfer, even in suspended SLG and BLG. The areas transferred range up ∼1000 µ m 2 on substrate. High-yield transfer of suspended ultra-thin 2DMs does not require critical point drying for areas up to 15 µ m 2 or suspension heights down to 160 nm. To demonstrate the method’s capabilities, we assembled on-substrate and suspended optical cavities tuning BLG’s Raman scattering intensity by factors of 19 and 4, respectively. This resilient and rapid 2DM transfer procedure will accelerate the fabrication of many heterostructures and permit versatile suspension of 2DMs for research in twistronics, straintronics, and nano-opto-electro-mechanical systems.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.018
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0200.001

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 head0.025
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; both teacher heads agree on what is shown here.

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

Citations8
Published2021
Admission routes2
Has abstractyes

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