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Record W4319341356 · doi:10.1063/5.0134605

Enhanced effective diffusion in sub-wavelength, axon-scale microchannels using surface acoustic waves

2023· article· en· W4319341356 on OpenAlexaff
Danli Peng, Wei Tong, David J. Collins, Michael R. Ibbotson, Steven Prawer, Melanie Stamp

Bibliographic record

VenuePhysics of Fluids · 2023
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Bio-sensing Technologies
Canadian institutionsUniversité de Sherbrooke
FundersNational Health and Medical Research CouncilAustralian Research CouncilUniversity of Melbourne
KeywordsMicrochannelMicroscale chemistryMicrofluidicsWavelengthPhysicsDiffusionAcoustic waveExcitationAcousticsMechanicsOptics

Abstract

fetched live from OpenAlex

Excitation using surface acoustic waves (SAW) has demonstrated efficacy in improving microscale particle/chemical transport due to its ability to generate microscale wavelengths. However, the effects of acoustic stimulation on transport processes along the length of sub-wavelength microchannels and their underlying mechanisms, essential for long-range transport, have not been examined in detail. In this work, we investigate diffusion along the length of subwavelength microchannels using experimental and simulation approaches, demonstrating enhanced transport under SAW excitation. The microchannel-based enhanced diffusion mechanisms are further studied by investigating the acoustic pressure and streaming fields, finding that the degree of enhancement is a function of applied power, microchannel dimensions, and viscosity. This microchannel-based diffusion enhancement approach is applicable to microfluidic and biomedical microscale transport enhancement, with the findings here being relevant to acoustic-based micro-mixing and neurodegenerative therapies.

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 score0.103
Threshold uncertainty score0.847

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)0.0000.000

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 head0.013
GPT teacher head0.226
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
Published2023
Admission routes1
Has abstractyes

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