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

Overview of attention for article published in Soft Matter, January 2017
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (80th percentile)
  • High Attention Score compared to outputs of the same age and source (96th percentile)

Mentioned by

twitter
10 tweeters
patent
1 patent

Citations

dimensions_citation
10 Dimensions

Readers on

mendeley
50 Mendeley

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Summary Twitter Patents Dimensions citations
Title
Honey, I shrunk the bubbles: microfluidic vacuum shrinkage of lipid-stabilized microbubbles
Published in
Soft Matter, January 2017
DOI 10.1039/c7sm00128b
Pubmed ID
28379267
Authors

Vaskar Gnyawali, Byeong-Ui Moon, Jennifer Kieda, Raffi Karshafian, Michael C. Kolios, Scott S. H. Tsai

Abstract

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.

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Twitter Demographics

The data shown below were collected from the profiles of 10 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 50 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 50 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 12 24%
Student > Bachelor 6 12%
Student > Master 6 12%
Researcher 5 10%
Professor > Associate Professor 3 6%
Other 5 10%
Unknown 13 26%
Readers by discipline Count As %
Engineering 17 34%
Chemistry 5 10%
Physics and Astronomy 4 8%
Chemical Engineering 3 6%
Materials Science 3 6%
Other 4 8%
Unknown 14 28%

Attention Score in Context

This research output has an Altmetric Attention Score of 10. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 13 August 2019.
All research outputs
#2,716,835
of 20,386,868 outputs
Outputs from Soft Matter
#323
of 7,606 outputs
Outputs of similar age
#54,097
of 281,395 outputs
Outputs of similar age from Soft Matter
#6
of 156 outputs
Altmetric has tracked 20,386,868 research outputs across all sources so far. Compared to these this one has done well and is in the 86th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,606 research outputs from this source. They receive a mean Attention Score of 3.2. This one has done particularly well, scoring higher than 95% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 281,395 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 80% of its contemporaries.
We're also able to compare this research output to 156 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 96% of its contemporaries.

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