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Record W2604683283 · doi:10.1039/c7sm00128b

Honey, I shrunk the bubbles: microfluidic vacuum shrinkage of lipid-stabilized microbubbles

2017· article· en· W2604683283 on OpenAlexaff
Vaskar Gnyawali, Byeong‐Ui Moon, Jennifer Kieda, Raffi Karshafian, Michael C. Kolios, Scott Tsai

Bibliographic record

VenueSoft Matter · 2017
Typearticle
Languageen
FieldEngineering
TopicUltrasound and Hyperthermia Applications
Canadian institutionsToronto Metropolitan UniversitySt. Michael's Hospital
Fundersnot available
KeywordsMicrobubblesShrinkageMicrofluidicsCavitationMaterials scienceUltrasoundNanotechnologyMechanicsComposite materialAcousticsPhysics

Abstract

fetched live from OpenAlex

We present a microfluidic technique that shrinks lipid-stabilized microbubbles from O(100) to O(1) μm in diameter - the size that is desirable in applications as ultrasound contrast agents. We achieve microbubble shrinkage by utilizing vacuum channels that are adjacent to the microfluidic flow channels to extract air from the microbubbles. We tune a single parameter, the vacuum pressure, to accurately control the final microbubble size. Finally, we demonstrate that the resulting O(1) μm diameter microbubbles have similar stability to microfluidically generated microbubbles that are not exposed to vacuum shrinkage. We anticipate that, with additional scale-up, this simple approach to shrink microbubbles generated microfluidically will be desirable in ultrasound imaging and therapeutic applications.

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 score0.508
Threshold uncertainty score0.850

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.217
Teacher spread0.207 · 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

Citations28
Published2017
Admission routes1
Has abstractyes

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