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Rickman, Jeffrey M.

Publications and source records attributed to Rickman, Jeffrey M..

Data-driven glass/ceramic science research: Insights from the glass and ceramic and data science/informatics communities

Data-driven science and technology have helped achieve meaningful results in areas such as materials/drug discovery and health care, but efforts to apply high-end data science algorithms to the areas of glass and ceramics are still limited. Many glass and ceramic researchers are interested in enhancing their work by using more data and data analytics to efficiently develop better functional materials. Simultaneously, the data science community is looking for a way to access materials data resources to test and validate their advanced computational learning algorithms. To bring together the glass/ceramic and data science communities to address this issue, The American Ceramic Society (ACerS) convened a Glass/Ceramics Data Science Workshop on February 6, 2018, at Nexight Group offices in Silver Spring, Maryland. The workshop, sponsored by the National Institute for Standards and Technology (NIST) Advanced Manufacturing Technologies (AMTech) program, brought together a select group of 20 key leaders in the data science, informatics, and glass/ceramics communities to identify the greatest opportunities and mechanisms for facilitating increased collaboration and coordination between these communities. This article summarizes workshop discussions about the current challenges that limit interactions and collaboration between the glass/ceramic and data science communities, opportunities for a coordinated approach that leverages existing knowledge in both communities, and a clear path toward the enhanced use of data science technologies for functional glass and ceramic research and development.

data science, ceramics, informatics↗

Selective encapsulation by Janus particles

We employ Monte Carlo simulation to examine encapsulation in a system comprising Janus oblate spheroids and isotropic spheres. More specifically, the impact of variations in temperature, particle size, inter-particle interaction range, and strength is examined for a system in which the spheroids act as the encapsulating agents and the spheres as the encapsulated guests. In this picture, particle interactions are described by a quasi-square-well patch model. This study highlights the environmental adaptation and selectivity of the encapsulation system to changes in temperature and guest particle size, respectively. Moreover, we identify an important range in parameter space where encapsulation is favored, as summarized by an encapsulation map. Finally, we discuss the generalization of our results to systems having a wide range of particle geometries.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Surface and grain boundary scattering in nanometric Cu thin films: A quantitative analysis including twin boundaries

The relative contributions of various defects to the measured resistivity in nanocrystalline Cu were investigated, including a quantitative account of twin-boundary scattering. It has been difficult to quantitatively assess the impact twin boundary scattering has on the classical size effect of electrical resistivity, due to limitations in characterizing twin boundaries in nanocrystalline Cu. In this study, crystal orientation maps of nanocrystalline Cu films were obtained via precession-assisted electron diffraction in the transmission electron microscope. These orientation images were used to characterize grain boundaries and to measure the average grain size of a microstructure, with and without considering twin boundaries. The results of these studies indicate that the contribution from grain-boundary scattering is the dominant factor (as compared to surface scattering) leading to enhanced resistivity. The resistivity data can be well-described by the combined Fuchs–Sondheimer surface scattering model and Mayadas–Shatzkes grain-boundary scattering model using Matthiessen's rule with a surface specularity coefficient of p = 0.48 and a grain-boundary reflection coefficient of R = 0.26.

36 MATERIALS SCIENCE↗