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Author ORCID Identifier


Open Access Dissertation

Document Type


Degree Name

Doctor of Philosophy (PhD)

Degree Program


Year Degree Awarded


Month Degree Awarded


First Advisor

Lori S. Goldner

Second Advisor

Anthony D. Dinsmore

Third Advisor

Narayanan Menon

Fourth Advisor

Sarah L. Perry

Subject Categories

Biological and Chemical Physics | Numerical Analysis and Scientific Computing | Optics | Probability | Statistical, Nonlinear, and Soft Matter Physics


Aqueous water-in-oil nanoemulsions have emerged as a versatile tool for use in microfluidics, drug delivery, single-molecule measurements, and other research. Nanoemulsions are often prepared with perfluorocarbons which are remarkably biocompatbile due to their stability, low surface tension, lipophobicity, and hydrophobicity. Therefore it is often assumed that droplet contents are unperturbed by the perfluorinated surface. However, in microemulsions, which are similar to nanoemulsions, it is known that either the pH of the aqueous phase or the ionization constants of encapsulated molecules are different from bulk solution. There is also recent evidence of low pH in perfluorinated aqueous nanoemulsions. The current underlying theory is that hydroxide ions aggregate at the oil-water interface leaving the bulk of the emulsion more acidic than usual. In this work, I measured the pH of aqueous nanoemulsions and sub-micron emulsions prepared by ultrasonication using Fluorinert FC-40 and a nonionic surfactant, PFPE–PEG–PFPE. To measure pH I measured a fluorescence emission ratio of a pH sensitive fluorophore, either fluorescein or difluorofluorescein (commonly called Oregon Green 488), with two apparatus: a fluorimeter for measuring ensemble pH, and a fluorescence microscope for measuring pH and size of single-droplets simultaneously. After lookup of emission ratio in a predetermined instrument–fluorophore calibration, the pH is determined. For fluorimeter measurements I developed a novel bayesian analytical model that accounts for the aqueous solution buffer capacity giving meaningful uncertainty estimates in the pH measurements. Fits to the fluorimeter calibration curve using currently established models of fluorescein were unsuccessful in obtaining a perfect fit therefore it is likely that these models are still incomplete; a heuristic model was used instead. For droplet measurements I obtained two data series as a function of sodium hydroxide concentration and as a function of surfactant concentration. Assuming hydroxide ions aggregate at the droplet surface both series predict a surface charge density of 0.04 OH nm−2 to 0.4 OH nm−2. The measurements indicated a decrease in charging with an increase in surfactant concentration and an increase in charging with an increase in sodium hydroxide concentration. The primary goal of single-droplet measurements was to measure pH as a function of size. Measuring pH involved a dual-view optical setup, affine transformation of image data, and three dimensional (position and time) lookup of emission ratio in the instrument–fluorophore calibration. Measuring droplet size involved single-particle tracking to obtain particle mean square displacements (MSDs) and fitting of the MSDs. In total I have data on 12785 droplets using many aqueous sample solution conditions and emulsion preparation conditions. Measurements predicted a surface charge density of 0.08 OH nm−2 to 0.32 OH nm−2 with the same trends in surfactant concentration and buffer concentration as in the fluorimeter measurements. As expected there was a decrease in charging with an increase in size. To mitigate surface charging I recommend working with 4 wt% surfactant concentration or greater


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Creative Commons Attribution-Noncommercial 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 4.0 License.

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