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Record W2088029716 · doi:10.1142/s0218126608004290

EXPLOITING SONOLUMINESCENCE TO REALIZE A MEMS ULTRASONIC SENSOR

2008· article· en· W2088029716 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Circuits Systems and Computers · 2008
Typearticle
Languageen
FieldMaterials Science
TopicUltrasound and Cavitation Phenomena
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsSonoluminescenceMaterials scienceUltrasonic sensorMicroelectromechanical systemsOptoelectronicsPhotodetectorTransducerAcousticsOpticsCavitationPhysics

Abstract

fetched live from OpenAlex

The design of a MEMS ultrasonic sensor has been presented that exploits the Single Bubble Sonoluminescence (SBSL) phenomenon to realize an energy transduction mechanism from acoustical to electrical domain. In the developed scheme, highly stable laser like short duration light pulses are emitted when ultrasound waves strike a thermally generated microbubble stabilized in a confined volume of 1% xenon-enriched water. The emitted light pulses are detected by an integrated profiled silicon type photodetector to generate ultrastable 100 picoseconds duration current pulses per acoustical cycle. The sensor exhibits energy amplification during the transduction process itself that is not achievable by conventional types of MEMS or non-MEMS acoustical sensors. The cylindrical sensor geometry is 650 μm in diameter and 300 μm in height and is designed to have a sensitivity of 5.56 mA/atm/cycle. The sensor can be used in applications where detection of high pressure ultrasound waves is necessary or as an ultrastable very short duration current pulse generator for use in tissue and nerve repair or microsurgery.

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.

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 score0.790
Threshold uncertainty score0.425

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 head0.026
GPT teacher head0.232
Teacher spread0.206 · 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