1. Thomas Payne Jordan Key
Extracting morphological changes in nanocrystals using in situ liquid cell microscopy
Thomas Payne
Jordan Key

2. outline Introduction and Motivation Type of Linkage
Image Segmentation and Binarization
Two-Point Statistics
PCA
Model Building
Future Work

3. Introduction/Motivation
Palladium nanocrystals from H2PdCl4 solution
Nanocrystal size and morphology control properties, so being able to tailor the structure is highly desireable
Liquid cell microscopy opens up a wide range of in situ experiments, such as corrosion and nanocrystal synthesis from solution, allowing for nanoscale resolution of real-time events

4. Process-structure linkage
We aim to connect the process variables
Electron beam dosage
Exposure time
To the structure variables
Structural evolution of the nanocrystals

5. Data sets STEM videos of nanocrystal nucleation and growth
BF and HAADF
3 videos: on the order of frames
Input
Electron beam dosage
Exposure time
Output (images)
Microstructural evolution

6. Sample data frame
Not all frames will be completely refreshed (beam raster)
Some shadowing or blurring can be seen in the contrast

7. Frame extraction
Developed an algorithm to make manual extraction easier
Reduces number of frames to be manually searched from thousands to hundreds

8. Segmentation challenge
Binarize gray-scale images containing two phases (liquid and solid)
We explored several approaches:
Simple global thresholding, adaptive mean thresholding, adaptive Gaussian thresholding, etc.
For us, simple global thresholding was a poor solution

9. Segmentation solution – big disk filter
Extract fully refreshed frames
Currently manual but hope to automate
Convert to grayscale → rgb2gray()
Crop image
Big Disk Filter → strel(‘disk’,25,0)
Find average at every point in disk → imfilter(...)
Remove background
Remove noise via  median filter → medfilt2(...)
Find threshold → imageSegmenter()
Save binarized image → imwrite(im2bw(image,threshold))
Automatically process all the images going back and manually adjusting threshold as necessary*

10. Visualizing the big disk filter
Locate and remove disk shaped “structuring elements” representing high signal regions (larger length scale than individual particles )

11. Sample binarized image

12. Automated python pipeline
We now have routines that will load in all of the complete frames, segment and binarize them, and calculate two-point statistics
*Used Otsu’s method to calculate the global threshold for each image, rather than manually adjusting threshold for each image
Unfortunately we still have to manually extract the completely refreshed frames from the original data set, but automating that may be beyond the scope of this project

13. Two-point statistics
Only need one auto correlation or the cross correlation
Two-phase microstructure
Non-periodic boundary conditions
Visual inspection
Volume fraction
Radial symmetry

14. PCA For a long time ran into memory issues with the calculations
Looked into using the kernel trick or some other method to address this, for a time just used PACE
This issue has now been resolved and we can run PCA locally
Utilized python module sklearn to compute PCA

15. PCA
From the scree plot, we decide only first three pc scores are necessary, as the first pc score accounts for over 95% of the variance
PC Score
Percent Variance 1 96 2
2.5 3
0.5
Sum 99

16. PCA
We now have three basis vectors and the corresponding pc scores for every microstructure in the array
The first-term reconstruction captures most of the features of the two-point statistics, and the reconstructions get better with additional terms

17. PCA
Our scree plot indicated that three pc scores should be sufficient for reconstruction, and this is justified by the three-term reconstruction below
Comparing to the full two-point statistics, the reconstruction matches quite well

18. Model building We use a polynomial regression to fit our pc scores
Using various metrics like R-squared fit and mean absolute error, we determined that a 5th order polynomial was the appropriate choice for the first two scores

19. PC1
PC2
PC3

20. Model building
PC1
PC2
PC3

21. Potential Future work Improve upon the pipeline for automated analysis
Reduce information loss from segmentation
Rewrite Matlab code in python
Use model to predict nanocrystal growth with time for new dosage and compare morphologies reconstructed from 2-point statistics to experimental results
Connect morphology evolution with kinetic model from literature

22. Acknowledgements Questions?
We would like to thank Dr. Ray Unocic from Oak Ridge National Lab for providing the data utilized in this project.
Questions?

26. PC1
PC2

27. Matin suggestions
User messages – currently no way to reply, no threads
If I want to reply to a message, I have to go to someone’s profile and create a new message
Automated messages from MATIN – no content
With new forums posts being made, we are getting automated messages to our MATIN account but they have no content. A preview of their post or something would be nice
Suggest automated messages for wiki comments – using above improvement
If we got a message every time someone commented on our wikis like we do forums, that would be nice (if the message has content)