# Presentation: 261748

## Particle Analysis of Transmission Electron Microscopy Images

- NYCRI Student Researcher- Divya Krishna
- NASA Mentors- John DaPonte Ph.D.
- 	           			      Thomas Sadowski
- Team Members – Monica Sawicki, Lisa Marinella, Paidemwoyo Munhutu

## Nanotechnology [1]

- 1x 10-9 meters   (one billionth of a meter)
- A Human Hair is about 80,000 nanometers wide.
- The nail on your little finger is about ten million nanometers across.
- The material’s optical, electrical, mechanical, and chemical properties change at such minute sizes.
- For Example -  When aluminum is shrunk to about 20 – 30 nm it explodes thus making it ideal to add to rocket fuel.
- Effects of Nanotechnology include
- Stain-resistant nanopants made from fibers treated with fluorinated nanopolymer.
- Spheres of silica, coated with a thin layer of gold and are about 120 nanometers, can infiltrate tumors when injected through a bloodstream.
- Crude oil filter made from a white layer of zeolite nanocrystals that filters crude oil into diesel fuel.

## TEM(Transmission Electron Microscope)

- Creates a bright field image as electrons are transmitted through a sample which is under a vacuum.
- Electrons have wave properties which are small enough to detect nanoparticles vs. light waves which have a much higher wavelength.
- The electrons are shot down through a
- 	magnetic field until some of them
- 	penetrate through the sample.
- The final image shows darker regions
-  	where the electrons did not go through
- 	and lighter regions where the electrons
- 	were absorbed into the sample.

## Project Overview

- Platinum nanoparticle images were analyzed through an imaging software called ImageJ [2] to find particle size and size distribution.
- Several pre-processing techniques were experimented with including:
- Rolling Ball Algorithm
- Pseudo Flat Field Correction
- Reasons for pre-processing include elimination of:
- The Haloing effect
- Excess amount of noise
- Image contamination
- Several thresholding algorithms were experimented with including:
- Entropy Thresholding [3]
- Kittler Thresholding [4]
- Calvart-Riddler Thresholding [5]
- Difficulties arising with the use of thresholding algorithms include:
- Erosion of nanoparticles in binary image
- Expansion of nanoparticles in binary image
- Incorrect representation of original particle image

## Slide 5

## Diameter (nm)

- Original Monomer TEM Image

- Binary Image

- Enhanced Grey Scale Image

- Filtered Binary Image

- Frequency

## Original Polymer TEM Image  

- Original Polymer TEM Image

- Enhanced Grey Scale Image

- Diameter (nm)

- Frequency

- Filtered Binary Image

- Binary Image

## Current & Future Studies

- Acquiring known specimen images as controls for comparing and confirming accurate particle measurements.
- Known specimens- 90 nanometer Latex Spheres.
- Expansion into nanowires
- Gallium Nitride nanowires
- Two dimensional analysis: length, width
- Atomic Force Microscopy
- AFM creates a topographical map of a sample
- Antimony particles will be analyzed for particle size and distribution.

## References

- Kahn, Jennifer. “Nanotechnology” *National Geographic, ***June**, pages 100-119, 2006.
- 2. ImageJ - _[http://rsb.info.nih.gov/ij/](http://rsb.info.nih.gov/ij/)_
- 3. J.N. Kapur, P.K. Sahoo, and A.K.C. Wong, “A new method for gray-level picture thresholding using the entropy of the histogram,” Graph. 	Models Image Process. 29, 273-285 (1985)
- 4. J. Kittler and J. Illingworth, “On threshold selection using clustering criteria,” IEEE Trans. Syst. Man Cybern. SMC-15, 652-655 (1985)
- 5. T.W. Ridler and S. Calvard, “Picture thresholding using an iterative selction method,” IEEE Trans. Syst. Man Cybern. SMC-8, 630-632 (1978)