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Record W4313589673 · doi:10.1209/0295-5075/acb009

Fluctuation-dissipation in thermoelectric sensors

2023· article· en· W4313589673 on OpenAlexafffund
Ngoc Anh Minh Tran, Aditya S. Dutt, Nithin B Pulumati, Heiko Reith, An Hu, Alexandre Dumont, Kornelius Nielsch, A.–M. S. Tremblay, Gabi Schierning, Bertrand Reulet, Thomas Szkopek

Bibliographic record

VenueEurophysics Letters (EPL) · 2023
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Thermoelectric Materials and Devices
Canadian institutionsUniversité de SherbrookeMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaCanada First Research Excellence FundCanada Excellence Research Chairs, Government of CanadaDeutsche Forschungsgemeinschaft
KeywordsThermoelectric effectJohnson–Nyquist noiseCondensed matter physicsDimensionless quantityNoise (video)PhysicsDissipationFluctuation-dissipation theoremThermal fluctuationsFigure of meritVoltageMaterials scienceAmplitudeThermodynamicsOptoelectronicsQuantum mechanics

Abstract

fetched live from OpenAlex

Abstract Thermoelectric materials exhibit correlated transport of charge and heat. The Johnson-Nyquist noise formula 4 k B T R for the spectral density of voltage fluctuations accounts for fluctuations associated solely with Ohmic dissipation. Applying the fluctuation-dissipation theorem, we generalize the Johnson-Nyquist formula for thermoelectrics, finding an enhanced voltage fluctuation spectral density 4 k B T R (1 + Z D T ) at frequencies below a thermal cut-off frequency f T , where Z D T is the dimensionless thermoelectric device figure of merit. The origin of the enhancement in voltage noise is thermoelectric coupling of temperature fluctuations. We use a wideband , integrated thermoelectric micro-device to experimentally confirm our findings. Measuring the Z D T enhanced voltage noise, we experimentally resolve temperature fluctuations with a root mean square amplitude of at a mean temperature of 295 K. We find that thermoelectric devices can be used for thermometry with sufficient resolution to measure the fundamental temperature fluctuations described by the fluctuation-dissipation theorem.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.011
GPT teacher head0.237
Teacher spread0.226 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations6
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueEurophysics Letters (EPL)Same topicAdvanced Thermoelectric Materials and DevicesFrench-language works237,207