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(XFEL) allows it to outrun radiation damage in coherent diffractive imaging since elastic scattering terminates before photoelectron cascades commences. This “diffract-before-destroy” feature of XFEL opened up new opportunities for biological macromolecule imaging and structure studies by breaking the limit to spatial resolution imposed by the maximum dose that is allowed before radiation damage. However, data collection in serial femto-second crystallography (SFX) using XFEL is affected by a bunch of stochastic factors, which pose great challenges to the data analysis in SFX. These stochastic factors include crystal size, shape, random orientation, X-ray photon flux, position and energy spectrum. Monte-Carlo integration proves effective and successful in extracting the structure factors by merging all diffraction patterns given that the data set is sufficiently large to average out all stochastic factors. However, this approach typically requires hundreds of thousands of patterns collected from experiments. This dissertation explores both experimental and algorithmic methods to eliminate or reduce the effect of stochastic factors in data acquisition and analysis. Coherent convergent X-ray beam diffraction (CCB) is discussed for possibilities of obtaining single-shot angular-integrated rocking curves. It is also shown the interference between Bragg disks helps ab-initio phasing. Two-color diffraction scheme is proposed for time-resolved studies and general data collection strategies are discussed based on error metrics. A new auto-indexing algorithm for sparse patterns is developed and demonstrated for both simulated and experimental data. Statistics show that indexing rate is increased by 3 times for I3C data set collected from beam time LJ69 at Linac coherent light source (LCLS). Finally, dynamical inversion from electron diffraction is explored as an alternative approach for structure determination.
This project will be a tribute to my experiences as a person, a chef, and as an ASU student. During my time spent here at ASU I have met a diverse group of people that I call my friends. Every time we would spend time together, I would learn about their lives and the experiences they are going through at this university. Everyone I met had a different background, story, and experience. Some of these memorable nights would be spent at my place. Depending on the circumstance, I would cook for my friends, and every time I did, they were amazed by my craft. Growing up, my mother was always working in the realm of fundraising. Through her jobs, we both had the opportunity to meet and work with some of the best chefs the Phoenix valley had to offer. Chefs like Robert Irvine, Mario Batali, Beau MacMillan, Christopher Gross, Michael DiMaria, Eddie Matney, and more. As a child and teenager, my fascination with cooking and food stood out to these figures and many taught me various skills and techniques in the kitchen. I learned to do everything from properly julian tangerines to preparing beef tartar. I even developed from making lemonade on my own when I was two years old to working in a four star restaurant as a line chef at the age of 15. These memories I will be forever grateful for. Through these skills, I have impressed my friends with delicious meals at night. And as we matured through college both in age and living situations, many of my friends have asked to learn from me. The change from freshman dorms to our own houses and townhomes have offered an endless opportunity of options for meals. But, everyone has a different background and skill set when it comes to cooking. A few of my friends have never picked up a knife before and have claimed to “burn water in the microwave.” Others tend to challenge me in preparing meals in their own homes and together we have our own “cookoffs.” From person to person, and living quarter to living quarter, there are many challenges to cooking. This is why I have decided to take the knowledge from my Industrial Engineering classes, my personal cooking skills, and data collected from the student body to create a cookbook for the average ASU student. I plan to include recipes and techniques in the form of Standard Operating Procedures to ensure that the instructions are as easy to follow as they can be. The recipes and techniques I plan to include will encompass data I have collected from the student body. The data will focus around a few key components of any chef and kitchen: tools and appliances available, personal cooking skills, and personal cooking experience. To take on such a challenge, I plan to complete this thesis/creative project in a few direct steps. First and foremost, complete this prospectus (already completed), next, secure funding from ASU for a survey completion incentive. For this survey, I will need a minimum of $250 to distribute between 5 winners. The monetary incentive is to ensure that more than 30 pieces of data (survey responses) are collected from each grade level of students. Next I will send a survey that asks about the aforementioned topics. After the survey is complete, I will collect the data, analyze it, and hone in on the most important and available tools. Finally, I will write stories surrounding my chosen recipes and create said recipes.
Many photosystem II (PSII) dataset have been collected at XFELs, several of which are time-resolved (containing both dark and laser illuminated frames). Comparison of light and dark datasets requires understanding systematic errors that can be introduced during data analysis. This dissertation describes data analysis of PSII datasets with a focus on the effect of parameters on later results. The influence of the subset of data used in the analysis is also examined and several criteria are screened for their utility in creating better subsets of data. Subsets are compared with Bragg data analysis and continuous diffuse scattering data analysis.
A new tool, DatView aids in the creation of subsets and visualization of statistics. DatView was developed to improve the loading speed to visualize statistics of large SFX datasets and simplify the creation of subsets based on the statistics. It combines the functionality of several existing visualization tools into a single interface, improving the exploratory power of the tool. In addition, it has comparison features that allow a pattern-by-pattern analysis of the effect of processing parameters. \emph{DatView} improves the efficiency of SFX data analysis by reducing loading time and providing novel visualization tools.
