Project 10: Data Storage and Analysis Framework for Semiconductor Nanocrystals Used in Bioimaging

Co-Mentor: Andre Schleife (Materials Science and Engineering)
Social Impact: By exploring systematic exchange of data and workflows, this project provides insights and best practices for the future of collaborative research. Providing access to sample specific data and analysis to the international community will accelerate deployment of novel semiconductor nanocrystals for bioimaging.

Project description: Light-emitting molecules are a central technology in biology and medicine that provide the ability to optically tag proteins and nucleic acids that mediate human disease. In particular, fluorescent dyes are a key part of molecular diagnostics and optical imaging reagents. We recently made major breakthroughs in engineering fluorescent semiconductor nanocrystals to increase the number of distinct molecules that can be accurately measured, far beyond what is possible with such organic dye molecules. We aim to develop nanocrystals that are able to distinguish diseased from healthy tissue and determine how the complex genetics underlying cancer respond to therapy, using measurement techniques and microscopes that are already widely accessible.
In order to achieve this goal, we need to understand a complex design space, that includes size, shape, composition, and internal structure of the different nanocrystals. Students in this team will work with computational and experimental researchers in several departments in order to establish a database to store, share, and catalog optical properties and other relevant data describing semiconductor nanocrystals. This requires developing schemas and analysis workflows that can be efficiently shared between multiple researchers. Eventually, both the data and the workflows will be made available to the general public.

Students will first identify all information that will need to be included in this catalogue. Students will then write JSON and python code and interface with Globus and the Materials Data Facility. They will create well-documented iPython notebooks that operate directly on the Globus file structure and run in the web browser. Students will also develop code that automatically analyzes data stored in the facility, e.g. to verify and validate experimental and computational results against each other. This project is highly interdisciplinary and students will work with a team of researchers in bioengineering, materials science, mechanical engineering, and NCSA.