Dataset Information

A Stochastic Model for Nucleation Kinetics Determination in Droplet-Based Microfluidic Systems.

ABSTRACT: The measured induction times in droplet-based microfluidic systems are stochastic and are not described by the deterministic population balances or moment equations commonly used to model the crystallization of amino acids, proteins, and active pharmaceutical ingredients. A stochastic model in the form of a Master equation is formulated for crystal nucleation in droplet-based microfluidic systems for any form of nucleation rate expression under conditions of time-varying supersaturation. An analytical solution is provided to describe the (1) time evolution of the probability of crystal nucleation, (2) the average number of crystals that will form at time t for a large number of droplets, (3) the induction time distribution, and (4) the mean, most likely, and median induction times. These expressions are used to develop methods for determining the nucleation kinetics. Nucleation kinetics are determined from induction times measured for paracetamol and lysozyme at high supersaturation in an evaporation-based high-throughput crystallization platform, which give low prediction errors when the nucleation kinetics were used to predict induction times for other experimental conditions. The proposed stochastic model is relevant to homogeneous and heterogeneous crystal nucleation in a wide range of droplet-based and microfluidic crystallization platforms.

SUBMITTER: Goh L

PROVIDER: S-EPMC2953805 | biostudies-literature | 2010 May

REPOSITORIES: biostudies-literature

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A Stochastic Model for Nucleation Kinetics Determination in Droplet-Based Microfluidic Systems.

Goh Limay L Chen Kejia K Bhamidi Venkateswarlu V He Guangwen G Kee Nicholas C S NC Kenis Paul J A PJ Zukoski Charles F CF Braatz Richard D RD

Crystal growth & design 20100501 6

The measured induction times in droplet-based microfluidic systems are stochastic and are not described by the deterministic population balances or moment equations commonly used to model the crystallization of amino acids, proteins, and active pharmaceutical ingredients. A stochastic model in the form of a Master equation is formulated for crystal nucleation in droplet-based microfluidic systems for any form of nucleation rate expression under conditions of time-varying supersaturation. An anal ...[more]

PMID: 20953348

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Project description:Acoustophoretic forces have been successfully implemented into droplet-based microfluidic devices to manipulate droplets. These acoustophoretic forces in droplet microfluidic devices are typically generated as in acoustofluidic devices through transducer actuation of a piezoelectric substrate such as lithium niobate (LiNbO3), which is inherently accompanied by the emergence of electrical fields. Understanding acoustophoretic versus dielectrophoretic forces produced by electrodes and transducers within active microfluidic devices is important for the optimization of device performance during design iterations. In this case study, we design microfluidic devices with a droplet injection module and report an experimental strategy to deduce the respective contribution of the acoustophoretic versus dielectrophoretic forces for the observed droplet injection. Our PDMS-based devices comprise a standard oil-in-water droplet-generating module connected to a T-junction injection module featuring actuating electrodes. We use two different electrode geometries produced within the same PDMS slab as the droplet production/injection channels by filling low-melting-point metal alloy into channels that template the electrode geometries. When these electrodes are constructed on LiNbO3 as the substrate, they have a dual function as a piezoelectric transducer, which we call embedded liquid metal interdigitated transducers (elmIDTs). To decipher the contribution of acoustophoretic versus dielectrophoretic forces, we build the same devices on either piezoelectric LiNbO3 or nonpiezo active glass substrates with different combinations of physical device characteristics (i.e., elmIDT geometry and alignment) and operate in a range of phase spaces (i.e., frequency, voltage, and transducer polarity). We characterize devices using techniques such as laser Doppler vibrometry (LDV) and infrared imaging, along with evaluating droplet injection for our series of device designs, constructions, and operating parameters. Although we find that LiNbO3 device designs generate acoustic fields, we demonstrate that droplet injection occurs only due to dielectrophoretic forces. We deduce that droplet injection is caused by the coupled dielectrophoretic forces arising from the operation of elmIDTs rather than by acoustophoretic forces for this specific device design. We arrive at this conclusion because equivalent droplet injection occurs without the presence of an acoustic field using the same electrode designs on nonpiezo active glass substrate devices. This work establishes a methodology to pinpoint the major contributing force of droplet manipulation in droplet-based acoustomicrofluidics.

Dataset Information

A Stochastic Model for Nucleation Kinetics Determination in Droplet-Based Microfluidic Systems.

Publications

A Stochastic Model for Nucleation Kinetics Determination in Droplet-Based Microfluidic Systems.

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