Predicting droplet breakup in a concentrated emulsion flowing in a microfluidic system

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Abstract/Contents

Abstract
In recent years, droplet microfluidics has been increasingly popular in chemistry and biology where biological or chemical samples are encapsulated in nL-pL droplets. Some typical applications are using droplet microfluidics as a flow cytometer, as a drug discovery platform, for nanoparticle synthesis, and for tissue engineering. Unlike solid wells in multi-well plates, droplets are prone to instabilities at the liquid--liquid interface. They can undergo coalescence or break-up, and compromise the accuracy of the assay. While coalescence has been avoided by using appropriate stabilizers, the break-up of droplets—especially those within a concentrated emulsion—remains a limiting factor affecting the throughput of the assay, in particular during droplet content interrogation step. Unfortunately, previous studies on single droplet break-up cannot provide a thorough explanation as to why droplets break in highly concentrated emulsion flow in confined space. Furthermore, most present literature studies emulsion interactions and rheology in the macro-scale and don't study droplet-droplet interactions and individual droplet break-up within a highly concentrated emulsion flowing in a confined space. Thus, this work aims to study the physics of droplet break-up in a highly concentrated emulsion flowing into a narrow constriction. This work is categorized into three parts: (1) Quantify the droplet break-up probability at various flow parameters, channel geometries, and emulsion properties (2) Study the effects of drop pair interactions in a highly concentrated emulsion flow that leads to droplet break-up (3) Study the effects of drop shapes in a highly concentrated emulsion flow that leads to droplet break-up For the first part, the statistics of droplet break-up within a highly concentrated emulsion entering a narrow constriction at various flow parameters, channel geometries, and emulsion properties. It was found that there exist a scaling law for the break-up probability at various capillary number. For the second part, the drop pair interactions as they enter the narrow constriction are studied. It was found that the relative drop pair locations as the drops are entering the narrow constriction is a dominant factor in determining the drop break-up outcome. Three regimes were found where drops will either have a break, not break, and bistable outcome. For the third part, the drop shape as they enter the narrow constriction are studied. It was found that there are a diverse range of drop shapes occurring as the drops are entering the narrow constriction. This leads to drastically different stress balances for each individual drops as they enter the narrow constriction. For this part, a machine learning model and training strategy is proposed to describe droplet shapes and predict its break-up outcome accurately. In conclusion, this work represents a critical step towards the understanding of the physics governing drop instability in a highly concentrated emulsions flowing into a narrow constriction

Description

Type of resource text
Form electronic resource; remote; computer; online resource
Extent 1 online resource
Place California
Place [Stanford, California]
Publisher [Stanford University]
Copyright date 2019; ©2019
Publication date 2019; 2019
Issuance monographic
Language English

Creators/Contributors

Author Khor, Jian Wei
Degree supervisor Tang, Sindy (Sindy K.Y.)
Thesis advisor Tang, Sindy (Sindy K.Y.)
Thesis advisor Cai, Wei, 1977-
Thesis advisor Dabiri, John O. (John Oluseun)
Degree committee member Cai, Wei, 1977-
Degree committee member Dabiri, John O. (John Oluseun)
Associated with Stanford University, Department of Mechanical Engineering.

Subjects

Genre Theses
Genre Text

Bibliographic information

Statement of responsibility Jian Wei Khor
Note Submitted to the Department of Mechanical Engineering
Thesis Thesis Ph.D. Stanford University 2019
Location electronic resource

Access conditions

Copyright
© 2019 by Jian Wei Khor
License
This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).

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