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Record W3049230043 · doi:10.1088/1361-6528/abaf1e

Modelling thermoelectric transport in III–V nanowires using a Boltzmann transport approach: a review

2020· review· en· W3049230043 on OpenAlexafffund
Ara Ghukasyan, Ray LaPierre

Bibliographic record

VenueNanotechnology · 2020
Typereview
Languageen
FieldMaterials Science
TopicAdvanced Thermoelectric Materials and Devices
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsNanowireThermoelectric effectSeebeck coefficientMaterials scienceThermal conductivityCondensed matter physicsBoltzmann constantBoltzmann equationElectron transport chainFigure of meritThermoelectric materialsElectronTransport theoryDopingNanotechnologyThermodynamicsOptoelectronicsStatistical physicsPhysicsQuantum mechanicsChemistry

Abstract

fetched live from OpenAlex

A review of models for determining the thermoelectric transport coefficients [Formula: see text] (Seebeck coefficient), [Formula: see text] (electrical conductivity), and [Formula: see text] (electronic thermal conductivity) is presented, for the cases of bulk and nanowire structures, along with derivations and a discussion of calculation methods. Results for the transport coefficients in GaAs, InAs, InP and InSb are used to determine the thermoelectric figure of merit, where an enhancement by two orders of magnitude is found for the nanowire case as compared with the bulk. The optimal electron concentration is determined as a function of nanowire diameter for both background and modulation doped nanowires.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.959
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.054
GPT teacher head0.300
Teacher spread0.247 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations8
Published2020
Admission routes2
Has abstractyes

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