Date of Thesis

Spring 2021

Description

This paper details a new technique to measure the mechanical properties of ETTMP PEGDA hydrogels using Hertz Contact Theory and simultaneously analyze both the model drug release and gel erosion in situ. This method involves curing a drug loaded hydrogel in a standard cuvette and placing a glass bead and phosphate buffer solution (PBS). Over time, the cross-linked network of the hydrogel breaks down, and, as a result, the ball sinks into the hydrogel. This method provides a macroscopic and inexpensive way to continuously and passively measure properties of the hydrogel as the hydrogel degrades. By plotting both the hydrogel erosion and model drug release of the hydrogel, the full dynamic release and degradation is simultaneously evaluated over a period of time. A machine learning algorithm is implemented to predict the behavior of the hydrogel under different experimental conditions. Data from the experiments trains a machine learning model and creates an optimized decision tree which uses mean square error analysis to predict the appropriate polymer weight percent of the hydrogel vessel based on the properties of the drug enclosed and desired degradation time.

Keywords

hydrogel degradation, mechanical properties, Hertz Contact Theory, machine learning, drug release

Access Type

Honors Thesis

Degree Type

Bachelor of Science in Chemical Engineering

Major

Chemical Engineering

Minor, Emphasis, or Concentration

Computer Science

First Advisor

Erin Jablonski

Second Advisor

Brandon Vogel

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