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Record W3077228099 · doi:10.1109/pn50013.2020.9167002

Nanowatt Thermal Radiation Sensing using Silicon Nitride Nanomechanical Resonators

2020· article· en· W3077228099 on OpenAlexaff
Nikaya Snell, Chang Zhang, Gengyang Mu, Raphaël St-Gelais

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMechanical and Optical Resonators
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsResonatorMaterials scienceBolometerOptoelectronicsTerahertz radiationInfraredThermal conductivityThermalSilicon nitridePhotonicsRadiationThermal radiationSiliconDetection limitOpticsPhysicsDetectorComposite materialChemistry

Abstract

fetched live from OpenAlex

Nanomechanical resonators are immune to electrical Johnson noise [1], making them a promising solution for infrared and terahertz radiation sensing beyond the performance of traditional thermal sensors [2] (e.g., bolometers). Here, we demonstrate detection of thermal radiation using thin (100 nm), large area (3 mm × 3 mm) SiN membrane resonators at room temperature. Our measurements demonstrate a detection limit on the order of 1 nW, with a characteristic response time of 85 ms. These preliminary measurements are comparable to state-of-the art commercial thermal sensors of similar surface area and response time (e.g., 5 nW detection limit reported in [3]).

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.480
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.026
GPT teacher head0.251
Teacher spread0.225 · 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

Citations4
Published2020
Admission routes1
Has abstractyes

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