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Record W4312982294 · doi:10.1109/pn56061.2022.9908390

Radiative Cooling of a Silicon Nitride Nanomechanical Resonator

2022· article· en· W4312982294 on OpenAlexaff
Alexandre Bouchard, Timothy Hodges, Michel Stéphan, Lixue Wu, Triantafillos Koukoulas, Richard Green, Raphaël St-Gelais

Bibliographic record

Venue2022 Photonics North (PN) · 2022
Typearticle
Languageen
FieldEngineering
TopicThermal Radiation and Cooling Technologies
Canadian institutionsNational Research Council CanadaMétis National CouncilUniversity of Ottawa
Fundersnot available
KeywordsRadiative coolingMicroscale chemistrySilicon nitrideRadiative transferMaterials scienceThermal radiationSiliconOptoelectronicsThermalMechanicsOpticsThermodynamicsPhysics

Abstract

fetched live from OpenAlex

Radiative cooling allows for the temperature of an object to diminish below ambient temperature by coupling it with the coldness of outer space via thermal radiation. Creating local thermal gradients on-chip with this phenomenon could potentially enable microscale energy conversion devices running on ambient heat. Here, we demonstrate localized radiative cooling of an on-chip silicon nitride membrane by up to 6.4 K at night, and 5.4 K during the day, under direct sunlight. This is in agreement with our theoretical model, which predicts a temperature drop of 5 K - 9 K for our experimental parameters, and a maximal drop of 15 K in ideal field of view and atmospheric conditions.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.137
Threshold uncertainty score0.734

Codex and Gemma teacher scores by category

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

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 head0.007
GPT teacher head0.191
Teacher spread0.183 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations0
Published2022
Admission routes1
Has abstractyes

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