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Record W4381598922 · doi:10.1002/adma.202303416

Roll‐to‐Roll Printing of Anomalous Nernst Thermopile for Direct Sensing of Perpendicular Heat Flux

2023· article· en· W4381598922 on OpenAlexfundno aff
Hirokazu Tanaka, Tomoya Higo, Ryota Uesugi, K. Yamagata, Yosuke Nakanishi, Hironobu Machinaga, Satoru Nakatsuji

Bibliographic record

VenueAdvanced Materials · 2023
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Thermoelectric Materials and Devices
Canadian institutionsnot available
FundersJST-Mirai ProgramDivision of Materials ResearchJapan Society for the Promotion of ScienceCore Research for Evolutional Science and TechnologyUniversity of TokyoCanadian Institute for Advanced Research
KeywordsThermopileNernst effectMaterials scienceNernst equationPerpendicularFlux (metallurgy)Heat fluxThermoelectric effectOptoelectronicsNanotechnologyMechanicsThermodynamicsOpticsHeat transferMetallurgyElectrodePhysics

Abstract

fetched live from OpenAlex

The anomalous Nernst effect (ANE) converts heat flux perpendicular to the plane into electricity, in sharp contrast with the Seebeck effect (SE), enabling mass production, large area, and flexibility of their devices through ordinary thin-film fabrication techniques. Heat flux sensors, one of the most promising applications of ANE, are powerful devices for evaluating heat flow and can lead to energy savings through efficient thermal management. In reality, however, SE caused by the in-plane heat flux is always superimposed on the measurement signal, making it difficult to evaluate the perpendicular heat flux. Here, ANE-type heat flux sensors that selectively detect a perpendicular heat flux are fabricated by adjusting the net Seebeck coefficient in their thermopile circuit with mass-producible roll-to-roll sputtering methods. The direct sensing of perpendicular heat flux using ANE-based flexible thermopiles, as well as their simple fabrication process, paves the way for the practical application of thin-film 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.013
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.012
GPT teacher head0.270
Teacher spread0.258 · 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

Citations49
Published2023
Admission routes1
Has abstractyes

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