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Gravity-Driven Multiple Effect Thermal System (G-METS) distillation for metals purification 

The atomic structure of FLiNaK and its evolution with temperature are examined with x-ray scattering and molecular dynamics (MD) simulations in the temperature range 460–636 °C. In accord with previous studies, it’s observed that the average nearest-neighbor (NN) cation-anion coordination number increases with increasing cation size, going from ∼4 for Li-F to ∼6.4 for K-F. In addition, we find that there is a coupled change in local coordination geometry – going from tetrahedral for Li-F to octahedral for Na to very disordered quasi-cuboidal for K. The varying geometry and coordination distances for the cation-anion pairs cause a relatively constant F-F next-nearest neighbor (NNN) distance of approximately 3.1 Å. This relatively fixed distance allows the F anions to assume an overall correlated structure very similar to that of a hard-sphere liquid with an extended radius which is beyond the normal F ion size but reflects the cation-anion coordination requirements. Careful consideration of the evolution of the experimental atomic distribution functions with increasing temperature shows that the changes in correlation at each distance can be understood within the context of broadening asymmetric neighbor distributions. Within the temperature range studied, the evolution of F-F correlations with increasing temperature is consistent with changes expected in a hard-sphere liquid simply due to decreasing density. 

Although faculty-centered pedagogies are endemic across undergraduate science, technology, engineering, and mathematics education, there is increasing interest in active learning approaches. As discipline-based educational research in mechanical engineering continues to assess strategies for improving student learning and development, researchers need data collection tools that ameliorate issues of bias, minimize costs (e.g. time and student attention), and provide reliable data that has been validated within the disciplinary context. This study analyzes the validity and reliability of a commonly used survey, the Students’ Assessment of their Learning Gains (SALG). Data from seven Introduction to Statics courses at two universities were used to identify and confirm the latent constructs of the measure and to assess their reliability and criterion validity. Results demonstrated that four scales—active learning, concept knowledge and skills, self-efficacy, and feedback mechanisms—explain the majority of variation in the SALG survey in relation to the teaching and learning of statics. These scales were statistically validated and shown to accurately capture the criterion they represent. The primary advantage of the SALG is that it is less burdensome to students, who are only required to spend 10 to 15 min once at the end of the course to complete the survey, rather than spending more time with longer surveys or with those that require completion at multiple points in time. The tool is therefore also less disruptive to the class, which may make it more likely that faculty will be willing to include data collection efforts in their courses. 

DOI: 10.1007/978-3-030-92559-8_5 The sixth Intergovernmental Panel on Climate Change report (IPCC) recently released predicts a deep reduction in emissions to meet global goals of 1.5 °C reduction in temperature. It states that concentrations of CO₂ have continuously increased in the atmosphere reaching averages of 410 ppm in 2019. Therefore, it becomes imperative to reduce CO₂ in any way possible. Silicon, which is an important material for renewable energy, electronics, and metallurgy, is primarily produced by the carbothermic reduction of quartz. This metallurgical grade silicon is then refined by the Siemens Process to solar grade silicon using hydrogen chloride. The by-product of trichlorosilane from this process is highly volatile and unstable. This work aims to achieve the above process of reduction in a single step using electrochemistry. This would eliminate multiple steps and save energy and cost and reduce emissions if a suitable inert anode is used in production. Understanding electrochemical cell characteristics therefore is needed to prove and scale this technology. Macroscopic models help engineers to design, develop, and improve the efficiency of electrochemical cells. They solve conservation equations of mass, momentum, and energy and help determine electrode current distribution, fluid flow, heat distribution, and stability of the cell. They also help in correlating experimental work and understanding measurements in cells from a lab scale to a plant scale. However, they do not predict the microstructure and plating of material on the cathode. This can be calculated using phase field models. These phase field models predict interface stability and deposition morphology in the cell. In this work, we present these models in addition to proof-of-concept experiments.