Employing Confidence Intervals in Electron Microscopy Digital Image Analysis to Promote Better Analytical Practices

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Master of Chemical Sciences Capstone Projects
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transmission electron microscopy
digital image analysis
Data Science
Materials Chemistry
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As the range of applications for nanoparticle systems continues to expand, the importance of providing reliable, informative, quantitative characterization for nanomaterial categorization and quality control continues to increase. Transmission Electron Microscopy (TEM) is a standard characterization technique that provides nanoscale image representations of a thin nanomaterial sample and has the potential to provide quantitative information with substantial digital image analysis. Modern semi-automatic TEM image analysis processes, such as the popular software ImageJ, aim to improve on outdated manual processes by incorporating user input with automated algorithms, decreasing the potential for human error and time expense. These processes segregate particles from the background in grayscale TEM images based on pixel intensity or particle shape, then calculate attributes such as size parameters from the derived shapes. However, in attempting to create processes that require little effort and time for the user, current image analysis procedures often undervalue the user’s knowledge of the system. Because the automated algorithms are expected to incorporate information about the system more suited to human determination, particles are often misidentified, resulting in inaccurate calculations. Furthermore, the automated algorithms are hidden from the user, making discussion of data manipulation biases arduous. To address these limitations, a novel TEM image analysis process was developed by integrating a traditional data analytics perspective. To provide feedback on the reliability of quantified size parameters, the presented process introduces confidence intervals based on image contrast and noise. These percentages represent the clarity of the image and variability between particles, allowing the user to choose what areas of the image they want to include in analysis. Encompassed in an easy-to-use MATLAB® App dashboard, the process allows the user to customize a more effective TEM image analysis process based on confidence interval feedback. In this project, a GdF3:Yb/Er rhombus nanoplatelet system and two Fe3O4 spherical systems were analyzed with the MATLAB® App to quantify size parameters comparable to standard ImageJ analysis. Additionally, the application’s ability to reveal image properties using confidence intervals is showcased by probing the effects of pre-processing regimes, magnification variation, and shape matching on size parameter reliability.

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