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Record W2947237153 · doi:10.1021/acs.nanolett.9b01661

Theoretical Design of Topological Heteronanotubes

2019· article· en· W2947237153 on OpenAlexafffund
Chen Hu, Vincent Michaud-Rioux, Yao Wang, Hong Guo

Bibliographic record

VenueNano Letters · 2019
Typearticle
Languageen
FieldMaterials Science
TopicGraphene research and applications
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaFonds Québécois de la Recherche sur la Nature et les Technologies
KeywordsTopology (electrical circuits)Carbon nanotubeNanoelectronicsMaterials scienceTopological defectNanotechnologyCoaxialBoron nitrideNanoscopic scalePhysicsCondensed matter physicsComputer scienceMathematics

Abstract

fetched live from OpenAlex

We propose and investigate the idea of topological heteronanotubes (THTs) for realizing an one-dimensional (1D) topological material platform that can pave the way to low-power carbon nanoelectronics at room temperature. We predict that the coaxial double-wall heteronanotube, a carbon nanotube (CNT) inside a boron nitride nanotube (BNNT), can act as a THT. Dissipationless topological conducting pathways on the THT are protected by a valley-dependent topological invariance that originates from local topological phase transitions of the CNT modulated by the CNT-BNNT interaction. Spiral THTs, where topological current flows spirally around the tube, function as nanoscale solenoids to induce remarkable magnetic fields due to the dense moiré nanopatterning. The generality and robustness of the THT materials are demonstrated by investigating different tube diameters, tube indexes, and tube types as well as topological-pathway orientations through first principles.

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 categoriesInsufficient payload (model declined to judge)
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 score0.100
Threshold uncertainty score0.997

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.016
GPT teacher head0.259
Teacher spread0.244 · 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.

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

Citations27
Published2019
Admission routes2
Has abstractyes

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