This EAGER award supports research and education involving a materials research - data science collaboration kindled at the MATDAT18 Datathon event focused on utilizing data analytics to amplify the capabilities of microscopies for materials. New scientific tools such as new kinds of microscopes are creating ever larger volumes of data, often in new formats. New computer programs are needed to make this data accessible to scientists and to allow researchers to gain the most insight from the acquired data. This project will focus on merging data science tools with new forms of microscopy. As a short, exploratory project, this research will develop open-source data-science-based tools to allow researchers to make better use of data coming from a new infrared microscopy method when applied to soft plastic electronic materials in the presence of electron and ion charges so that these electronic materials can be used as more effective bioelectronic transistors, sensors, and next-generation computing elements. Importantly, the project will provide a graduate student with advanced training at the interface between data science and polymer materials science, while also providing mentoring opportunities for summer undergraduate research students.
This EAGER award supports research and education involving a materials research - data science collaboration kindled at the MATDAT18 Datathon event. The goal of this project is to merge data-science tools with new nanoscale sub-diffraction infrared imaging capabilities to extract fundamental structure-function relationships in complex polymer semiconductor films and composites, particularly in the context of organic electrochemical transistors (OECTs). OECTs are a promising platform for neural signal transduction and biosensing applications in challenging aqueous environments. They typically comprise a semiconducting polymer in contact with an ion source that serves to modulate charge transport in the polymer. Currently, progress in the field has been held up by a limited understanding of structure-function properties at the nanoscale, particularly by a lack correlated chemical composition with ion and charge transport. This project will use a new chemical mapping tool, photoinduced force microscopy (PiFM), and correlated AFM data as a platform for developing advanced data analysis methods that can quantify relationships among nanoscale properties. The project will develop open source tools for handling the complex hyperspectral data sets generated on polymers. Specifically, the team will: (1) develop new methods for reliably discriminating chemical components in soft conducting polymer systems using hyperspectral PiFM spectra; (2) develop advanced regression beyond common linear methods to extract quantitative structure-function relationships using correlated multimodal AFM data cubes; (3) incorporate the methods developed into open source packages for wider adoption in the AFM community. These results should open new doors to enable broader advances in polymer bioelectronics and electronics.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
