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Record W1967896665 · doi:10.1088/2043-6262/5/3/035010

A fluid density sensor based on a resonant tube

2014· article· en· W1967896665 on OpenAlexfundno aff
Yong Zhu, Dzung Viet Dao, Peter Woodfield

Bibliographic record

VenueAdvances in Natural Sciences Nanoscience and Nanotechnology · 2014
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Capillary Electrophoresis Applications
Canadian institutionsnot available
FundersSimon Fraser UniversityGriffith University
KeywordsResonance (particle physics)Tube (container)Materials scienceAcousticsLow frequencySample (material)Frequency shiftComputer scienceOpticsPhysicsTelecommunicationsComposite materialAtomic physics

Abstract

fetched live from OpenAlex

A fluid density sensor based on resonance frequency change of a metallic tube is presented. The sensor has been developed without using a complex micro-fabrication process. The sensor is able to identify fluid types/contaminations and improve the performance by reducing testing time, decreasing complexity of testing equipment and reducing sample sizes. The sensor can measure the resonance frequency of its own structure and determine the change in resonance frequency due to the subsequent sample inside the tube. Numerical modelling, analytical modelling and physical testing of a prototype sensor showed comparable results for both the magnitude and resonance frequency shift. The modelling results yielded a resonance frequency shift of 200 Hz from 9.87 kHz to 9.67 kHz after the water was filled into the tube. The actual testing illustrated a resonance frequency change of 280 Hz from 9.11 kHz to 8.83 kHz. The ultimate aim of the work is to determine resonance frequencies of desired samples at a level that could detect genetic disease on a cellular level.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.309
Threshold uncertainty score0.481

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 studies0.0000.001
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 head0.004
GPT teacher head0.216
Teacher spread0.213 · 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

Citations1
Published2014
Admission routes1
Has abstractyes

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