Paper Number
PO28
Session
Poster Session
Title
Improving analysis methods for Dripping-onto-Substrate (DoS) extensional rheology measurements
Presentation Date and Time
October 13, 2021 (Wednesday) 6:30
Track / Room
Poster Session / Ballroom 1-2-3-4
Authors
- Lauser, Kathleen T. (University of Minnesota, Chemical Engineering and Materials Science)
- Calabrese, Michelle A. (University of Minnesota, Chemical Engineering and Materials Science)
- Zhang, Diana Y. (University of Minnesota, Chemical Engineering and Materials Science)
Author and Affiliation Lines
Kathleen T. Lauser, Michelle A. Calabrese and Diana Y. Zhang
Chemical Engineering and Materials Science, University of Minnesota, Minneapolis, MN 55414
Speaker / Presenter
Lauser, Kathleen T.
Keywords
experimental methods; biological materials; polymer solutions
Text of Abstract
Dripping-onto-Substrate (DoS) extensional rheology can be used to measure the capillary-driven thinning of a variety of fluids, including polymer, micellar, and protein solutions. The thinning of a liquid bridge is recorded by a high-speed camera, and custom scripts convert images to the minimum radius profile which can then be fit to obtain extensional rheological properties. However, data analysis methods for DoS may be inconsistent or visually-based, which makes analysis prone to human error. For example, trials can have considerable variation in symmetry of the filament and contact angle on the substrate. Additionally, visual selection of the startpoint frame can dramatically change the total break-up time, adding uncertainty when comparing trials. While these subtleties may be less important for fluids exhibiting elastic-like behaviors, these issues are non-trivial when analyzing low viscosity and low elasticity fluids. In this work, we develop metrics for assessing quality of DoS measurements, and analyze correlations between parameters such as symmetry, contact angle, and aspect ratio. Our automated scripts can detect patterns in liquid bridge images, automatically determine the thinning startpoint, and give quantitative metrics for parameters like filament symmetry. By incorporating automated data analysis and quantitative metrics to analyze data quality, more consistent measurements and analysis between DoS trials are obtained.