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Record W2784516183 · doi:10.1021/acsnano.7b06430

Large Enhancement of Thermal Conductivity and Lorenz Number in Topological Insulator Thin Films

2018· article· en· W2784516183 on OpenAlexafffund
Zhe Luo, Jifa Tian, Shouyuan Huang, Mithun Srinivasan, Jesse Maassen, Yong P. Chen, Xianfan Xu

Bibliographic record

VenueACS Nano · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicTopological Materials and Phenomena
Canadian institutionsDalhousie University
FundersDivision of Emerging FrontiersNatural Sciences and Engineering Research Council of CanadaDefense Advanced Research Projects Agency
KeywordsTopological insulatorCondensed matter physicsMaterials scienceThermal conductivitySurface statesThermal conductionDecoupling (probability)Wiedemann–Franz lawThin filmTopology (electrical circuits)NanotechnologySurface (topology)PhysicsComposite materialGeometry

Abstract

fetched live from OpenAlex

Topological insulators (TI) have attracted extensive research effort due to their insulating bulk states but conducting surface states. However, investigation and understanding of thermal transport in topological insulators, particularly the effect of surface states, are lacking. In this work, we studied thickness-dependent in-plane thermal and electrical conductivity of Bi 2 Te 2 Se TI thin films. A large enhancement in both thermal and electrical conductivity was observed for films with thicknesses below 20 nm, which is attributed to the surface states and bulk-insulating nature of these films. Moreover, a surface Lorenz number much larger than the Sommerfeld value was found. Systematic transport measurements indicated that the Fermi surface is located near the charge neutrality point (CNP) when the film thickness is below 20 nm. Possible reasons for the large Lorenz number include electrical and thermal current decoupling in the surface state Dirac fluid, and bipolar diffusion transport. A simple computational model indicates that the surface states and bipolar diffusion indeed can lead to enhanced electrical and thermal transport and a large Lorenz number.

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 score0.287
Threshold uncertainty score0.994

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)0.0070.000

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 head0.015
GPT teacher head0.266
Teacher spread0.251 · 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

Citations35
Published2018
Admission routes2
Has abstractyes

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