The research goal of this collaborative research award is to develop a statistics-guided framework for significantly enhancing the efficiency of nano-synthesis and the accuracy of nano-characterization in nanomanufacturing. The research seeks to produce transformative computational and analytic methods for advancing the reproducibility, reliability, efficiency and precision of nanomaterials. These new methods include a level-expansion experimental design for sequential synthesis of nanomaterials, a sequential tapering method for accurate characterization of nanomaterial surface potential with massive data and a functional data analysis approach for identifying phase truncation in nano-characterization. These methods can significantly enhance both the lab-scale development of nanoparticles, nanowires and complex nanoarchitectures, and industrial scale nanomanufacturing.
If successful, the results of this research are expected to accelerate the scale-up and standardization of experiments in nanomanufacturing. These results will be disseminated broadly to the nanotechnology community. Beyond nanomanufacturing, the statistical and computational methods developed in this research will have broad applications to other fields in manufacturing and enterprise systems such as multi-stage manufacturing processes, logistics and health care. Undergraduate and graduate students will benefit from this research through rigorous training in statistics and nanotechnology. A dynamic platform will be established to teach undergraduate minority students advanced applied sciences and provide research experiences.
