Public Abstract

DE-SC0025716: 4D-STEM Nano-Characterization Infrastructure to Enhance Materials Research for Underrepresented Minorities

Award Status: Active

Institution: Florida A&M University, Tallahassee, FL
UEI: W8LKB16HV1K5
DUNS: 623751831

Most Recent Award Date: 01/17/2025
Number of Support Periods: 1
PM: Zhu, Jane

Current Budget Period: 02/01/2025 - 01/31/2026
Current Project Period: 02/01/2025 - 01/31/2028
PI: Kametani, Fumitake

Supplement Budget Period: N/A

Public Abstract

This research project aims to significantly enhance the nano-characterization infrastructure at Florida A&M University (FAMU) by adding 4D Scanning Transmission Electron Microscopy (STEM) capability to study advanced high-temperature superconductors and nanostructured high-performance composites and structural materials with advanced data analysis. 4D STEM, a technological breakthrough that uses a pixelated electron detector to capture a convergent beam electron diffraction pattern at each scan location, will open up a new level of quantitative characterization at FAMU, the largest public Historically Black College & University (HBCU). The mapping of atomic/nano-scale information by 4D STEM will resolve the nano-characterization needs to understand energy materials, including strain and stoichiometry variations at the nano and macro scales and the processing-microstructure-property relationships. By acquiring a new direct electron detector for 4D STEM and building the collaboration network with Center for Nanophase Materials Sciences (CNMS) at Oak Ridge National Laboratory (ORNL), this research project will provide FAMU students with access to a nationally competitive nanocharacterization capability and facilitate the engagement of underrepresented talent in the microscopy community. A major challenge of 4D STEM lies in handling the massive 4D datasets generated, which can reach terabyte scales for high-resolution experiments. This research project will create a pipeline of 4D STEM data analysis for underrepresented minority (URM) students. Advanced computing resources using modern graphics processing units enable hybrid algorithms that combine multislice and Bloch wave methods for improved accuracy and speed of the data analysis. The rich 4D-STEM data set enables orders of magnitude more efficient recording of complex nanostructural information and opens up new horizons in making new discoveries from “big data” using, for example, machine learning. These research efforts are strengthened by collaboration with ORNL. This research project will provide invaluable training opportunities for URM students and postdocs in nanoscience and nanotechnology research.

This research project aims to significantly enhance the nano-characterization infrastructure at Florida A&M University (FAMU) by adding 4D Scanning Transmission Electron Microscopy (STEM) capability to study advanced high-temperature superconductors and nanostructured high-performance composites and structural materials with advanced data analysis. 4D STEM, a technological breakthrough that uses a pixelated electron detector to capture a convergent beam electron diffraction pattern at each scan location, will open up a new level of quantitative characterization at FAMU, the largest public Historically Black College & University (HBCU). The mapping of atomic/nano-scale information by 4D STEM will resolve the nano-characterization needs to understand energy materials, including strain and stoichiometry variations at the nano and macro scales and the processing-microstructure-property relationships. By acquiring a new direct electron detector for 4D STEM and building the collaboration network with Center for Nanophase Materials Sciences (CNMS) at Oak Ridge National Laboratory (ORNL), this research project will provide FAMU students with access to a nationally competitive nanocharacterization capability and facilitate the engagement of underrepresented talent in the microscopy community. A major challenge of 4D STEM lies in handling the massive 4D datasets generated, which can reach terabyte scales for high-resolution experiments. This research project will create a pipeline of 4D STEM data analysis for underrepresented minority (URM) students. Advanced computing resources using modern graphics processing units enable hybrid algorithms that combine multislice and Bloch wave methods for improved accuracy and speed of the data analysis. The rich 4D-STEM data set enables orders of magnitude more efficient recording of complex nanostructural information and opens up new horizons in making new discoveries from “big data” using, for example, machine learning. These research efforts are strengthened by collaboration with ORNL. This research project will provide invaluable training opportunities for URM students and postdocs in nanoscience and nanotechnology research.