↓ Skip to main content

An Open Software Platform for the Automated Design of Paper-Based Microfluidic Devices

Overview of attention for article published in Scientific Reports, November 2017
Altmetric Badge

About this Attention Score

  • Good Attention Score compared to outputs of the same age (68th percentile)
  • Good Attention Score compared to outputs of the same age and source (68th percentile)

Mentioned by

twitter
5 tweeters

Citations

dimensions_citation
11 Dimensions

Readers on

mendeley
38 Mendeley
Title
An Open Software Platform for the Automated Design of Paper-Based Microfluidic Devices
Published in
Scientific Reports, November 2017
DOI 10.1038/s41598-017-16542-8
Pubmed ID
Authors

Nicholas S. DeChiara, Daniel J. Wilson, Charles R. Mace

Abstract

Paper-based microfluidic devices have many applications in biomedical and environmental analysis. However, the process of prototyping device designs can be tedious, error-prone, and time-consuming. Here, we present a cross-platform, open-source software tool-AutoPAD-developed to quickly create and modify device designs and provide a free alternative to commercial design software. The capabilities that we designed to be inherent to AutoPAD (e.g., automatic zone alignment and design refactoring) highlight its potential use in nearly any paper-based microfluidic device application and for creating nearly any desired design, which we demonstrate through the recreation of numerous device designs from the literature.

Twitter Demographics

The data shown below were collected from the profiles of 5 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 38 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 38 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 8 21%
Student > Master 6 16%
Student > Bachelor 5 13%
Other 4 11%
Professor > Associate Professor 4 11%
Other 9 24%
Unknown 2 5%
Readers by discipline Count As %
Engineering 9 24%
Chemistry 9 24%
Biochemistry, Genetics and Molecular Biology 4 11%
Nursing and Health Professions 2 5%
Medicine and Dentistry 2 5%
Other 6 16%
Unknown 6 16%

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 29 March 2018.
All research outputs
#3,828,808
of 13,853,539 outputs
Outputs from Scientific Reports
#23,618
of 69,762 outputs
Outputs of similar age
#125,946
of 400,307 outputs
Outputs of similar age from Scientific Reports
#4,124
of 13,234 outputs
Altmetric has tracked 13,853,539 research outputs across all sources so far. This one has received more attention than most of these and is in the 72nd percentile.
So far Altmetric has tracked 69,762 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 15.5. This one has gotten more attention than average, scoring higher than 65% 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 400,307 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 68% of its contemporaries.
We're also able to compare this research output to 13,234 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 68% of its contemporaries.