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Record W1883750693 · doi:10.1002/anie.201505817

Stable Encapsulated Air Nanobubbles in Water

2015· article· en· W1883750693 on OpenAlexafffund
Yu Wang, Guojun Liu, Heng Hu, Terry Yantian Li, Amer M. Johri, Xiaoyu Li, Jian Wang

Bibliographic record

VenueAngewandte Chemie International Edition · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicMinerals Flotation and Separation Techniques
Canadian institutionsKingston General HospitalQueen's University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsMicrobubblesSonicationMicrometerMaterials scienceCapillary actionNanotechnologyBubbleUltrasoundDrug deliveryDispersion (optics)Chemical engineeringChemistryChromatographyComposite materialOpticsRadiologyComputer scienceMedicine

Abstract

fetched live from OpenAlex

The dispersion into water of nanocapsules bearing a highly hydrophobic fluorinated internal lining yielded encapsulated air nanobubbles. These bubbles, like their micrometer-sized counterparts (microbubbles), effectively reflected ultrasound. More importantly, the nanobubbles survived under ultrasonication 100-times longer than a commercial microbubble sample that is currently in clinical use. We justify this unprecedented stability theoretically. These nanobubbles, owing to their small size and potential ability to permeate the capillary networks of tissues, may expand the applications of microbubbles in diagnostic ultrasonography and find new applications in ultrasound-regulated drug delivery.

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 categoriesInsufficient payload (model declined to judge)
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 score0.180
Threshold uncertainty score0.997

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)0.0040.001

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 head0.021
GPT teacher head0.263
Teacher spread0.242 · 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.

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

Citations19
Published2015
Admission routes2
Has abstractyes

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