ArticleLangmuir : the ACS journal of surfaces and colloids2022
Programmable Droplet Microfluidics Based on Machine Learning and Acoustic Manipulation.
Article in Langmuir : the ACS journal of surfaces and colloids, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 21 citations in OpenAlex.
- Thermal Tightrope: Trajectory Dynamics of Levitating Droplet Clusters under Variable Temperature Conditions.Langmuir : the ACS journal of surfaces and colloids · 2026Article
- Building like a Coral-Parallelized, Multiscale Biofabrication.Advanced materials (Deerfield Beach, Fla.) · 2026Review
- Investigating the metastability of amorphous calcium carbonate by droplet microfluidics experiments using machine learning.Scientific reports · 2025Article
- Streaming-based Tweezers for Routing, Engineering, and Manipulation of multiparticles: STREAM.Microsystems & nanoengineering · 2025Article
- Magnetic Cell-Mimetic Droplet Microrobots with Division and Exocytosis Capabilities.Research (Washington, D.C.) · 2025Article
- Automating life science labs at the single-cell level through precise ultrasonic liquid sample ejection: PULSE.Microsystems & nanoengineering · 2024Article
- Acoustofluidic Actuation of Living Cells.Micromachines · 2024Review
- Microsystem Advances through Integration with Artificial Intelligence.Micromachines · 2023Review
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Typical microfluidic devices are application-specific and have to be carefully designed to implement the necessary functionalities for the targeted application. Programmable microfluidic chips try to overcome this by offering reconfigurable functionalities, allowing the same chip to be used in multiple different applications. In this work, we demonstrate a programmable microfluidic chip for the two-dimensional manipulation of droplets, based on ultrasonic bulk acoustic waves and a closed-loop machine-learning-based control algorithm. The algorithm has no prior knowledge of the acoustic fields but learns to control the droplets on the fly. The manipulation is based on switching the frequency of a single ultrasonic transducer. Using this method, we demonstrate 2D transportation and merging of water droplets in oil and oil droplets in water, and we performed the chemistry that underlies the basis of a colorimetric glucose assay. We show that we can manipulate drops with volumes ranging from ∼200 pL up to ∼30 nL with our setup. We also demonstrate that our method is robust, by changing the system parameters and showing that the machine learning algorithm can still complete the manipulation tasks. In short, our method uses ultrasonics to flexibly manipulate droplets, enabling programmable droplet microfluidic devices.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.