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Record W2749789893 · doi:10.1109/ted.2017.2738838

Strain-Induced Armchair Graphene Nanoribbon Resonant-Tunneling Diodes

2017· article· en· W2749789893 on OpenAlex
Milad Zoghi, Arash Yazdanpanah Goharrizi

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Transactions on Electron Devices · 2017
Typearticle
Languageen
FieldMaterials Science
TopicGraphene research and applications
Canadian institutionsConcordia University
Fundersnot available
KeywordsResonant-tunneling diodeQuantum tunnellingDiodeMaterials scienceGrapheneGraphene nanoribbonsNon-equilibrium thermodynamicsCondensed matter physicsStrain (injury)Strain engineeringFormalism (music)OptoelectronicsNanotechnologyQuantum wellPhysicsQuantum mechanicsSilicon

Abstract

fetched live from OpenAlex

The electronic properties of armchair graphene nanoribbons (AGNRs) can be changed and modified under the uniaxial strain. Taking this advantage, we propose a new platform of AGNR-based resonant-tunneling diode (RTD) using the effects of strain for the first time. In this RTD platform, barrier regions are composed of strained AGNR, whereas channel is made up by pristine AGNR. The calculated results show that the double barrier quantum well is performed for such device, and negative differential resistance property appears in I-V characteristic. In addition, performance of strain-induced 12-AGNR-RTD is explored under the variation of strain percentage (ε). It is realized that the efficiency of such devices consist of peak to valley ratio can be engineered by setting strain percentage (ε) to appropriate orders. Tight binding model coupled with nonequilibrium Green's function formalism is derived for this paper.

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.

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 categoriesScience and technology studies
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 score0.043
Threshold uncertainty score0.999

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 studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.033
GPT teacher head0.311
Teacher spread0.278 · 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