ArticleScientific reports2020
A deep learning approach for designed diffraction-based acoustic patterning in microchannels.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Additive Manufacturing Techniques for Acoustic Hologram Lens Microfabrication.Micromachines · 2025Review
- AI-Enabled Microfluidics for Respiratory Pathogen Detection.Sensors (Basel, Switzerland) · 2025Review
- Improved acoustic holograms using simulated annealing.Biomicrofluidics · 2025Article
- Dynamic interface printing.Nature · 2024Article
- Acoustically shaped DNA-programmable materials.Nature communications · 2024Article
- Cavity-agnostic acoustofluidic manipulations enabled by guided flexural waves on a membrane acoustic waveguide actuator.Microsystems & nanoengineering · 2024Article
- Robust global arrangement by coherent enhancement in Huygens-Fresnel traveling surface acoustic wave interference field.Analytical and bioanalytical chemistry · 2024Article
- Programmable Acoustic Holography using Medium-Sound-Speed Modulation.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023Article
- Microsystem Advances through Integration with Artificial Intelligence.Micromachines · 2023Review
- Deep learning with microfluidics for on-chip droplet generation, control, and analysis.Frontiers in bioengineering and biotechnology · 2023Review
- Intelligent acoustofluidics enabled mini-bioreactors for human brain organoids.Lab on a chip · 2021Article
- Controlled Manipulation and Active Sorting of Particles Inside Microfluidic Chips Using Bulk Acoustic Waves and Machine Learning.Langmuir : the ACS journal of surfaces and colloids · 2021Article
- A new open-access platform for measuring and sharing mTBI data.Scientific reports · 2021Article
- Contactless acoustic micro/nano manipulation: a paradigm for next generation applications in life sciences.Proceedings. Mathematical, physical, and engineering sciences · 2020Review
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
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