A deep learning approach for designed diffraction-based acoustic patterning in microchannels
Name
s41598-020-65453-8.pdf
Description
Published version
Size
2 MB
Format
Adobe PDF
Checksum (MD5)
e20e77acc31643db4989639c77f50974
Author(s)
Raymond, Samuel J
Collins, David J
O’Rorke, Richard
Tayebi, Mahnoush
Ai, Ye
Williams, John
Date Issued
May 2020
Journal
Scientific Reports
Publisher
Springer Science and Business Media LLC
Citation
Raymond, S.J., Collins, D.J., O’Rorke, R. et al. A deep learning approach for designed diffraction-based acoustic patterning in microchannels. Sci Rep 10, 8745 (2020).
Version
Final published version
Abstract
© 2020, The Author(s). Acoustic waves can be used to accurately position cells and particles and are appropriate for this activity owing to their biocompatibility and ability to generate microscale force gradients. Such fields, however, typically take the form of only periodic one or two-dimensional grids, limiting the scope of patterning activities that can be performed. Recent work has demonstrated that the interaction between microfluidic channel walls and travelling surface acoustic waves can generate spatially variable acoustic fields, opening the possibility that the channel geometry can be used to control the pressure field that develops. In this work we utilize this approach to create novel acoustic fields. Designing the channel that results in a desired acoustic field, however, is a non-trivial task. To rapidly generate designed acoustic fields from microchannel elements we utilize a deep learning approach based on a deep neural network (DNN) that is trained on images of pre-solved acoustic fields. We use then this trained DNN to create novel microchannel architectures for designed microparticle patterning.
MIT Department
Massachusetts Institute of Technology. Department of Civil and Environmental Engineering
Massachusetts Institute of Technology. Center for Computational Science and Engineering
MIT-SUTD Collaboration
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1038/S41598-020-65453-8
