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Scattering strength of the scatterer inducing variability in graphene on silicon oxide

2016· article· en· W2279788240 on OpenAlexfundno aff
Jyoti Katoch, Duy Le, Simranjeet Singh, Rahul Rao, Talat S. Rahman, Masa Ishigami

Bibliographic record

VenueJournal of Physics Condensed Matter · 2016
Typearticle
Languageen
FieldMaterials Science
TopicGraphene research and applications
Canadian institutionsnot available
FundersDivision of ChemistryDivision of Materials ResearchCanadian Centre for Applied Research in Cancer ControlNational Science Foundation
KeywordsGrapheneOxideScatteringMaterials scienceSiliconIonHydrogenChemical physicsCarrier scatteringOptoelectronicsNanotechnologyPhysicsOpticsQuantum mechanics

Abstract

fetched live from OpenAlex

Large variability of carrier mobility of graphene-based field effect transistors hampers graphene science and technology. We show that the number of the scatterer responsible for the observed variability on graphene devices on silicon oxide can be determined by finding the number of hydrogen that can be chemisorbed on graphene. We use the relationship between the number of the scatterer and the mobility of graphene devices to determine that the variability-inducing scatterer possesses scattering strength 10 times smaller than that of adsorbed potassium atoms and 50 times smaller than that of ion-beam induced vacancies. Our results provide an important, quantitative input towards determining the origin of the variability.

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 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 score0.138
Threshold uncertainty score0.280

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.017
GPT teacher head0.260
Teacher spread0.243 · 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.

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

Citations4
Published2016
Admission routes1
Has abstractyes

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