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Record W3116263776 · doi:10.1021/acs.nanolett.0c03868

Fracture and Fatigue of Al<sub>2</sub>O<sub>3</sub>-Graphene Nanolayers

2020· article· en· W3116263776 on OpenAlexafffund
Maedeh Amirmaleki, Teng Cui, Yang Zhao, Jason Tam, Anukalp Goel, Yu Sun, Xueliang Sun, Tobin Filleter

Bibliographic record

VenueNano Letters · 2020
Typearticle
Languageen
FieldMaterials Science
TopicGraphene research and applications
Canadian institutionsWestern UniversityUniversity of Toronto
FundersUniversity of TorontoNatural Sciences and Engineering Research Council of CanadaOntario Ministry of Research, Innovation and Science
KeywordsGrapheneMaterials scienceFracture (geology)Composite materialCreepDeformation (meteorology)Stress (linguistics)Nanotechnology

Abstract

fetched live from OpenAlex

Al2O3-graphene nanolayers are widely used within integrated micro/nanoelectronic systems; however, their lifetimes are largely limited by fracture both statically and dynamically. Here, we present a static and fatigue study of thin (1–11 nm) free-standing Al2O3-graphene nanolayers. A remarkable fatigue life of greater than one billion cycles was obtained for films <2.2 nm thick under large mean stress levels, which was up to 3 orders of magnitude longer than that of its thicker (11 nm) counterpart. A similar thickness dependency was also identified for the elastic and static fracture behavior, where the enhancement effect of graphene is prominent only within a thickness of ∼3.3 nm. Moreover, plastic deformation, manifested by viscous creep, was observed and appeared to be more substantial for thicker films. This study provides mechanistic insights on both the static and dynamic reliability of Al2O3-graphene nanolayers and can potentially guide the design of graphene-based devices.

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)0.000
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 score0.027
Threshold uncertainty score0.899

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.019
GPT teacher head0.249
Teacher spread0.230 · 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

Citations16
Published2020
Admission routes2
Has abstractyes

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