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Record W4396610433 · doi:10.11159/icnnfc24.003

Graphene Nanoribbons for Bandgap Engineering

2024· article· en· W4396610433 on OpenAlexvenueno aff
Colm Durkan

Bibliographic record

VenueProceedings of the World Congress on Recent Advances in Nanotechnology · 2024
Typearticle
Languageen
FieldMaterials Science
TopicGraphene research and applications
Canadian institutionsnot available
Fundersnot available
KeywordsGraphene nanoribbonsBand gapOptoelectronicsMaterials scienceGrapheneWide-bandgap semiconductorEngineering physicsNanotechnologyPhysics

Abstract

fetched live from OpenAlex

Graphene is being touted as the wonder material of the 21st century due to its impressive electrical and mechanical properties.Whether scalable and economically viable devices that can outperform their conventional alternatives will emerge is still somewhat of an open question, but recent developments in wafer-scale production of graphene via CVD (Chemical Vapour Deposition) offer great promise.In this talk, we will look at three topics -(i) size-dependence of resistivity of graphene nanoribbons, which reveal some new phenomena, with scanning gate microscopy as a technique to explore edge effects, (ii) substrate-mediated device architectures and (iii) an AFM-based oxidative etching process used to create nanoribbons with widths down to below 10 nm.We show that the resistivity of graphene nanoribbons scales more strongly with size than in the case of metals, mostly due to the nature of graphene's Dirac Fermions, but also due to the emergence of a bandgap for widths below around 30 nm.We then experimentally demonstrate a graphene/ ferrolectric device, termed Ferrotronic (electronic effect from ferroelectric) device in which the band-structure of single-layer graphene is modified.The device architecture consists of graphene deposited on a ferroelectric substrate which encodes a periodic surface potential achieved through domain engineering.This structure takes advantage of the nature of conduction through graphene to modulate the Fermi velocity of the charge carriers by the variations in surface potential, leading to the emergence of energy mini-bands and a band gap at the superlattice Brillouin zone boundary.Thisrepresents a simple route to building circuits whose functionality is controlled by the underlying substrate.In the final part, we introduce an AFM-based technique for ultra-precise oxidative lithography of graphene based on tipinduced hydrolysis.Fig1.Left: Graphene Ferrotronic device architecture where a ferroelectric substrate is periodically poled in order to create a periodic potential; Right: Graphene device for testing width-dependent resistivity.

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: none
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

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 integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.002

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 head0.282
Teacher spread0.272 · 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".

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Citations0
Published2024
Admission routes1
Has abstractyes

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