The Woodruff School

SUMMARY

Morphology analysis of cellulose nanocrystals (CNCs) using Transmission Electron Microscopy (TEM) and Atomic Force Microscopy (AFM) images is an important step in the design and optimization of the processes employed in the manufacture and utilization of CNCs. Highly polydisperse nature and irregular rod-like shapes of CNCs are challenging the morphology analysis of CNC particles. Current protocols used in the analyses of CNC particle morphology for such microscopy images are largely manual and time-consuming, and often produce inconsistent results between different researchers. The proposed thesis aims to improve and standardize morphology analysis for AFM and TEM images of CNCs and subsequently promote CNC characterization for different applications. To achieve that, the thesis is divided into two research tasks. As the first task of this thesis, a semi-automated image analysis framework is developed to quantify the structure (particle size and grouping) of CNCs in an accurate, consistent, and time-efficient way. This framework is implemented by introducing a graphical user interface, CNC-SMART, which utilizes different automated and semi-automated image processing workflows for AFM and TEM image analysis. CNC-SMART can expeditiously process high-throughput image data using these workflows while being minimally impacted by human error and variability. The second task will be the utilization of the developed SMART system over proposed case studies so that the SMART approach can be adapted and improved for further research. The first case study will be a part of an ongoing inter-laboratory comparison research conducted by ten different research groups. SMART system will be tested on a very large dataset helping develop standards for CNC characterization. The second case study will focus on obtaining detailed morphology information (grouping and CNC-CNC connection) to understand the effect of asymmetrical flow field fractionation (AF4) process on the CNC structure and to calibrate AF4 process for achieving desired structure. The last case study will focus on structure-property relations where the material properties of interest will be rheological properties such as viscosity of CNC suspensions and optical properties such as transparency and color of CNC films and suspensions.

SUBJECT:	Ph.D. Proposal Presentation

BY:	Sezen Yucel

TIME:	Thursday, July 9, 2020, 2:30 p.m.

PLACE:	https://bluejeans.com/7649580204, NA

TITLE:	Semi-automatic morphology analysis approach for cellulose nanocrystals and correlations to process parameters and properties

COMMITTEE:	Dr. Surya R. Kalidindi, Chair (ME) Dr. Robert J. Moon (MSE) Dr. Kyriaki Kalaitzidou (ME) Dr. Karl I. Jacob (ME) Dr. Paul S. Russo (MSE)

SUMMARY Morphology analysis of cellulose nanocrystals (CNCs) using Transmission Electron Microscopy (TEM) and Atomic Force Microscopy (AFM) images is an important step in the design and optimization of the processes employed in the manufacture and utilization of CNCs. Highly polydisperse nature and irregular rod-like shapes of CNCs are challenging the morphology analysis of CNC particles. Current protocols used in the analyses of CNC particle morphology for such microscopy images are largely manual and time-consuming, and often produce inconsistent results between different researchers. The proposed thesis aims to improve and standardize morphology analysis for AFM and TEM images of CNCs and subsequently promote CNC characterization for different applications. To achieve that, the thesis is divided into two research tasks. As the first task of this thesis, a semi-automated image analysis framework is developed to quantify the structure (particle size and grouping) of CNCs in an accurate, consistent, and time-efficient way. This framework is implemented by introducing a graphical user interface, CNC-SMART, which utilizes different automated and semi-automated image processing workflows for AFM and TEM image analysis. CNC-SMART can expeditiously process high-throughput image data using these workflows while being minimally impacted by human error and variability. The second task will be the utilization of the developed SMART system over proposed case studies so that the SMART approach can be adapted and improved for further research. The first case study will be a part of an ongoing inter-laboratory comparison research conducted by ten different research groups. SMART system will be tested on a very large dataset helping develop standards for CNC characterization. The second case study will focus on obtaining detailed morphology information (grouping and CNC-CNC connection) to understand the effect of asymmetrical flow field fractionation (AF4) process on the CNC structure and to calibrate AF4 process for achieving desired structure. The last case study will focus on structure-property relations where the material properties of interest will be rheological properties such as viscosity of CNC suspensions and optical properties such as transparency and color of CNC films and suspensions.