Computational Materials Design for Ceramic Nuclear Waste Forms Using Machine Learning, First-Principles Calculations, and Kinetics Rate Theory

Abstract

Ceramic waste forms are designed to immobilize radionuclides for permanent disposal in geological repositories. One of the principal criteria for the effective incorporation of waste elements is their compatibility with the host material. In terms of performance under environmental conditions, the resistance of the waste forms to degradation over long periods of time is a critical concern when they are exposed to natural environments. Due to their unique crystallographic features and behavior in nature environment as exemplified by their natural analogues, ceramic waste forms are capable of incorporating problematic nuclear waste elements while showing promising chemical durability in aqueous environments. Recent studies of apatite- and hollandite-structured waste forms demonstrated an approach that can predict the compositions of ceramic waste forms and their long-term dissolution rate by a combination of computational techniques including machine learning, first-principles thermodynamics calculations, and modeling using kinetic rate equations based on critical laboratory experiments. By integrating the predictions of elemental incorporation and degradation kinetics in a holistic framework, the approach could be promising for the design of advanced ceramic waste forms with optimized incorporation capacity and environmental degradation performance. Such an approach could provide a path for accelerated ceramic waste form development and performance prediction for problematic nuclear waste elements.

Disease Diagnosis with Chemosensing, Artificial Intelligence, and Prospective Contributions of Nanoarchitectonics

“…Until now, scientists have achieved this through experience, intuition, and honed literature searches. In recent years, however, artificial intelligence (AI) technologies have advanced remarkably; machine learning (ML) [72][73][74][75][76][77][78][79] and materials informatics [80][81][82][83][84][85][86][87] now demonstrate considerable potential to support the work of scientists, on the premise of large amounts of accumulated data. This may give nanoarchitectonics, which aims to integrate many kinds of materials and diverse properties, a significant edge against the evolving challenges and demands of today.…”

Nonlinear Filter-Based Adaptive Output-Feedback Control for Uncertain Fractional-Order Nonlinear Systems with Unknown External Disturbance

“…In addition, many physical systems, such as circuit systems, can be expressed as FO nonlinear systems and are susceptible to electromagnetic perturbations from external environments. Electromagnetic interference is caused by the conduction and electrical radiation generated by devices similar to electromagnetic circles in the process of using current and voltage in the network environment [19][20][21], which has a destructive effect on electronic components and leads to abnormalities in the internal pulse clock system of electronic components. Consequently, it is necessary to address the disturbance suppression control for FO nonlinear systems with disturbances and modeling errors.…”