CAREER: Revealing the Atomistic Fundamentals of Probabilistic Strength Distributions in Nanomaterials via High-Throughput Experimentation

University Of Texas At Dallas, Richardson TX

Investigators

Abstract

This Faculty Early Career Development (CAREER) award will support research that contributes to the reliable design of nanostructured devices with broad applications in wearable medical equipment, next-generation electronics, and strong and lightweight materials. Nanomaterials are enablers of these technologies owing to their capacity to withstand high forces and deformations. This capacity, however, can be highly variable due to unwanted behaviors such as early failures arising from manufacturing conditions and defects, e.g., voids and cracks. Having clear and statistically significant measurements of this variability, mathematical tools to model it, and understanding the underlying factors that control it, are the goals of this research project. The generated knowledge will enable the creation of reliable designs, by accounting for and predicting this variability, while leveraging the outstanding properties of nanomaterials. The integrated education plan includes enhancement of existing curriculum with probabilistic descriptions of scientific phenomena, promotion of mechanical engineering among high school students through tutoring, lectures with hands-on experiments and field trips, and year-round mentoring of underrepresented minority students. Weibull’s distribution has traditionally been used to describe the statistical nature of strength in materials, but its foundational assumptions are incompatible at low submicron to nanometer length scale. Thus, it is a critical issue for the reliable design of micro- and nano-structured devices. The objective of this research is to investigate the probabilistic strength distribution of nanoscale materials and structures using high throughput testing to generate statistically significant data for evaluation and to offer new postulates that would form the bases of more accurate probabilistic strength theories. Merging self-assembly and patterning techniques and nanomechanical testing, strength data will be obtained from hundreds of nano-specimens such as nanowires and two-dimensional materials (e.g., graphene). Complementary characterization with various techniques (e.g., electron microscopy and Raman spectroscopy) will be used to understand the effect of atomistic defects on strength statistics. Existing and new probabilistic strength theories will be assessed against the experimental data for predictive accuracy. 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.