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Computational modeling of magnetic particle margination within blood flow through LAMMPS

Overview of attention for article published in Computational Mechanics, November 2017
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#17 of 256)
  • Good Attention Score compared to outputs of the same age (75th percentile)
  • Good Attention Score compared to outputs of the same age and source (71st percentile)

Mentioned by

news
1 news outlet

Citations

dimensions_citation
38 Dimensions

Readers on

mendeley
53 Mendeley
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Title
Computational modeling of magnetic particle margination within blood flow through LAMMPS
Published in
Computational Mechanics, November 2017
DOI 10.1007/s00466-017-1508-y
Authors

Huilin Ye, Zhiqiang Shen, Ying Li

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 53 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 53 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 18 34%
Student > Master 6 11%
Researcher 5 9%
Student > Doctoral Student 4 8%
Lecturer 4 8%
Other 10 19%
Unknown 6 11%
Readers by discipline Count As %
Engineering 22 42%
Materials Science 5 9%
Biochemistry, Genetics and Molecular Biology 4 8%
Physics and Astronomy 4 8%
Chemistry 3 6%
Other 6 11%
Unknown 9 17%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 7. 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 24 August 2018.
All research outputs
#4,243,742
of 23,100,534 outputs
Outputs from Computational Mechanics
#17
of 256 outputs
Outputs of similar age
#76,784
of 327,038 outputs
Outputs of similar age from Computational Mechanics
#2
of 7 outputs
Altmetric has tracked 23,100,534 research outputs across all sources so far. Compared to these this one has done well and is in the 80th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 256 research outputs from this source. They receive a mean Attention Score of 3.7. This one has done particularly well, scoring higher than 91% 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 327,038 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 75% of its contemporaries.
We're also able to compare this research output to 7 others from the same source and published within six weeks on either side of this one. This one has scored higher than 5 of them.