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Record W2799618375 · doi:10.1063/1.5022097

Altering thermal transport by strained-layer epitaxy

2018· article· en· W2799618375 on OpenAlexafffund
Tahereh Majdi, Souvik Pal, Anders Hafreager, Sohail Murad, Rakesh P. Sahu, Ishwar K. Puri

Bibliographic record

VenueApplied Physics Letters · 2018
Typearticle
Languageen
FieldMaterials Science
TopicThermal properties of materials
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaCompute CanadaSandia National LaboratoriesMcMaster University
KeywordsMaterials scienceThin filmEpitaxyStrain engineeringThermoelectric effectPhononThermal conductivityCondensed matter physicsMolecular beam epitaxyComposite materialOptoelectronicsLayer (electronics)Nanotechnology

Abstract

fetched live from OpenAlex

Since strain changes the interatomic spacing of matter and alters electron and phonon dispersion, an applied strain can modify the thermal conductivity k of a material. We show how the strain induced by heteroepitaxy is a passive mechanism to change k in a thin film. Molecular dynamics simulations of the deposition and epitaxial growth of ZnTe thin films provide insights into the role of interfacial strain in the conductivity of a deposited film. ZnTe films grow strain-free on lattice-matched ZnTe substrates, but similar thin films grown on a lattice-mismatched CdTe substrate exhibit ∼6% biaxial in-plane tensile strain and ∼7% uniaxial out-of-plane compressive strain. In the T = 700 K–1100 K temperature range, the conductivities of strained ZnTe layers decrease to ∼60% of their unstrained values. The resulting understanding of dk/dT shows that strain engineering can be used to alter the performance of a thermal rectifier and also provides a framework for enhancing thermoelectric 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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.004
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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.002

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 head0.217
Teacher spread0.201 · 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; both teacher heads agree on what is shown here.

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
Published2018
Admission routes2
Has abstractyes

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