3DμF - Interactive Design Environment for Continuous Flow Microfluidic Devices

Sanka, Radhakrishna; Lippai, Joshua; Samarasekera, Dinithi; Nemsick, Sarah; Densmore, Douglas

doi:10.1038/s41598-019-45623-z

Cited by 28 publications

(18 citation statements)

References 37 publications

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“…That language can be compiled into a parameter-free version of a netlist file called MINT 65 . MINT can be read by emerging software tools including 3DμF 66 . Using this file, 3DμF's design abilities, and DAFD's parameter exploration, a fully realized design can be created quickly without having to have monolithic designs that encode both the structure and function in a nonmodifiable manner.…”

Section: Discussionmentioning

confidence: 99%

Machine learning enables design automation of microfluidic flow-focusing droplet generation

et al. 2021

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Droplet-based microfluidic devices hold immense potential in becoming inexpensive alternatives to existing screening platforms across life science applications, such as enzyme discovery and early cancer detection. However, the lack of a predictive understanding of droplet generation makes engineering a droplet-based platform an iterative and resource-intensive process. We present a web-based tool, DAFD, that predicts the performance and enables design automation of flow-focusing droplet generators. We capitalize on machine learning algorithms to predict the droplet diameter and rate with a mean absolute error of less than 10 μm and 20 Hz. This tool delivers a user-specified performance within 4.2% and 11.5% of the desired diameter and rate. We demonstrate that DAFD can be extended by the community to support additional fluid combinations, without requiring extensive machine learning knowledge or large-scale data-sets. This tool will reduce the need for microfluidic expertise and design iterations and facilitate adoption of microfluidics in life sciences.

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Section: Discussionmentioning

confidence: 99%

Machine learning enables design automation of microfluidic flow-focusing droplet generation

et al. 2021

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“…Pneumatic FSMs are also well suited to bring powerful new capabilities to the embedded control of soft robotics, where pneumatic circuitry is already attracting substantial interest [1,24,25,26]. Adoption of pneumatic digital circuitry can be catalyzed by the development of rapid prototyping approaches [27] and design automation tools [28] to accelerate development cycles.…”

Section: Discussionmentioning

confidence: 99%

Pneumatic Computers for Embedded Control of Microfluidics

Ahrar¹,

Raje²,

Lee³

et al. 2022

Preprint

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Alternative computing approaches that interface readily with physical systems are well suited for embedded control of those systems. We demonstrate finite state machines implemented as pneumatic circuits of microfluidic valves, and we employ these controllers to direct microfluidic liquid handling procedures such as serial dilution on the same chip. These monolithic integrated systems require only power to be supplied externally, in the form of a vacuum source. User input can be provided directly to the chip by covering pneumatic ports with a finger. State machines with up to four bits of state memory are demonstrated, and next-state combinational logic can be fully reprogrammed by changing the hole-punch pattern on a membrane in the chip. These pneumatic computers demonstrate a new framework for the embedded control of physical systems.

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“…NOTE: Microfluidic chips are generated by casting PDMS (Polydimethylsiloxane) in a mold. Molds can be designed using CAD software (e.g., uFlow or 3DµF 30 ). A repository of free designs is provided by the MIT (Metafluidics.org).…”

Section: Generation Of the Chipmentioning

confidence: 99%

A Microfluidics Approach for the Functional Investigation of Signaling Oscillations Governing Somitogenesis

Oostrom

Meijer

Sonnen

2021

JoVE

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Periodic segmentation of the presomitic mesoderm of a developing mouse embryo is controlled by a network of signaling pathways. Signaling oscillations and gradients are thought to control the timing and spacing of segment formation, respectively. While the involved signaling pathways have been studied extensively over the last decades, direct evidence for the function of signaling oscillations in controlling somitogenesis has been lacking. To enable the functional investigation of signaling dynamics, microfluidics is a previously established tool for the subtle modulation of these dynamics. With this microfluidics-based entrainment approach endogenous signaling oscillations are synchronized by pulses of pathway modulators. This enables modulation of, for instance, the oscillation period or the phase-relationship between two oscillating pathways. Furthermore, spatial gradients of pathway modulators can be established along the tissue to study how specific changes in the signaling gradients affect somitogenesis.The present protocol is meant to help establish microfluidic approaches for the first-time users of microfluidics. The basic principles and equipment needed to set up a microfluidic system are described, and a chip design is provided, with which a mold for chip generation can conveniently be prepared using a 3D printer. Finally, how to culture primary mouse tissue on a microfluidic chip and how to entrain signaling oscillations to external pulses of pathway modulators are discussed.This microfluidic system can also be adapted to harbor other in vivo and in vitro model systems such as gastruloids and organoids for functional investigation of signaling dynamics and morphogen gradients in other contexts.

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3DμF - Interactive Design Environment for Continuous Flow Microfluidic Devices

Cited by 28 publications

References 37 publications

Machine learning enables design automation of microfluidic flow-focusing droplet generation

Machine learning enables design automation of microfluidic flow-focusing droplet generation

Pneumatic Computers for Embedded Control of Microfluidics

A Microfluidics Approach for the Functional Investigation of Signaling Oscillations Governing Somitogenesis

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