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Thermoelectric Power Factor Limit of a 1D Nanowire

2018· article· en· W2963200423 on OpenAlexfundno aff
I‐Ju Chen, A. M. Burke, Artis Svilans, Heiner Linke, Claes Thelander

Bibliographic record

VenuePhysical Review Letters · 2018
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Thermoelectric Materials and Devices
Canadian institutionsnot available
FundersResearch Executive AgencyEnergimyndighetenVetenskapsrådetSeventh Framework ProgrammeKnut och Alice Wallenbergs StiftelseCNIB
KeywordsLimit (mathematics)Thermoelectric effectNanowireMaterials scienceSeebeck coefficientPower factorCondensed matter physicsPower (physics)PhysicsNanotechnologyThermodynamics

Abstract

fetched live from OpenAlex

In the past decade, there has been significant interest in the potentially advantageous thermoelectric properties of one-dimensional (1D) nanowires, but it has been challenging to find high thermoelectric power factors based on 1D effects in practice. Here we point out that there is an upper limit to the thermoelectric power factor of nonballistic 1D nanowires, as a consequence of the recently established quantum bound of thermoelectric power output. We experimentally test this limit in quasiballistic InAs nanowires by extracting the maximum power factor of the first 1D subband through I-V characterization, finding that the measured maximum power factors conform to the theoretical limit. The established limit allows the prediction of the achievable power factor of a specific nanowire material system with 1D electronic transport based on the nanowire dimension and mean free path. The power factor of state-of-the-art semiconductor nanowires with small cross section and high crystal quality can be expected to be highly competitive (on the order of mW/m K^{2}) at low temperatures. However, they have no clear advantage over bulk materials at, or above, room temperature.

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 score0.011
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0010.001

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 head0.013
GPT teacher head0.286
Teacher spread0.273 · 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

Citations43
Published2018
Admission routes1
Has abstractyes

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