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Record W2581519295 · doi:10.1063/1.4975347

Single laser modulated drive and detection of a nano-optomechanical cantilever

2017· article· en· W2581519295 on OpenAlexafffund
Vincent T. K. Sauer, Zhu Diao, Jocelyn N. Westwood‐Bachman, M. R. Freeman, Wayne K. Hiebert

Bibliographic record

VenueAIP Advances · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMechanical and Optical Resonators
Canadian institutionsNational Institute for NanotechnologyUniversity of Alberta
FundersNational Research Council CanadaAlberta InnovatesAlberta Innovates - Health SolutionsAlberta Innovates - Technology FuturesNatural Sciences and Engineering Research Council of CanadaCMC MicrosystemsCanadian Institute for Advanced Research
KeywordsCantileverLaserPhysicsNoise (video)OpticsOptomechanicsLaser power scalingLimit (mathematics)Laser Doppler vibrometerMaterials scienceOptoelectronicsResonatorComputer science

Abstract

fetched live from OpenAlex

To reduce the complexity in a nano-optomechanical system a pump and probe scheme using only a single input laser is used to both coherently pump and probe the nanomechanical device. The system operates similarly to the traditional two laser system, but instead of using a constant power to probe the device and a separate, modulated laser to drive it with an optical gradient force, a single laser is utilized for both functions. A model of the measurement scheme’s response is developed which matches the experimental data obtained in the optomechanical Doppler regime and low cavity power limit. As such, the unconventional response still yields useful device information such as the resonant frequency of the device and its mechanical quality factor. The device is driven with low noise and its frequency is tracked using a phase-locked loop. This demonstrates its potential use for dynamic frequency measurements such as nanomechanical inertial mass loading. In such a system, the estimated mass resolution of the device is 6 zg and consistent with other detection methods.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.509
Threshold uncertainty score0.253

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)0.0000.000

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 head0.009
GPT teacher head0.252
Teacher spread0.243 · 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 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

Citations8
Published2017
Admission routes2
Has abstractyes

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