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Ionic transport is a critical property for the glass industry, since emerging applications such as sensors, batteries, and electric melting are based on the phenomenon. Short-range interactions (anion-charge carrier) have not been able to explain the total activation barrier observed experimentally, and, as such, it is critical to understand the larger role of all ions in a glass, not just the carrier and the ‘site’ ions. This research focuses on the role of network formers and their impact on diffusion in glasses, something that current models lack an explicit explanation of. Atomistic simulations with randomly generated parameters for the cation potentials and classical simulations were used to determine the diffusion coefficients and activation energies for synthetic network formers. Using this database, explainable machine learning algorithms were employed to explore network former interactions and determine which parameters are the most influential for ion diffusion. Results suggest that the bond length of the cations changes the geometry of the structure contributing the greatest to cation-modifier interactions. 

Proton therapy has potential for high precision dose delivery, provided that high accuracy is achieved in imaging. Currently, X-ray based techniques are preferred for imaging prior to proton therapy, and the stopping power conversion tables cause irreducible uncertainty. The proposed proton imaging methods aim to reduce this source of error, as well as lessen the radiation exposure of the patient. CARNA is a homogeneous compact calorimeter that utilizes a novel high density scintillating glass as an active medium. The compact design and unique geometry of the calorimeter eliminate the need for a tracker system and allow it to be directly attached to a gantry. Thus, giving CARNA potential to be used for insitu imaging during the hadron therapy, possibly to detect the prompt gammas. The novel glass development and the traditional image reconstruction studies performed with CARNA have been reported before. However, to improve the image reconstruction, a machine learning implementation with CARNA is reported. A proof-of-concept Artificial Neural Network, is shown to efficiently predict the density and the shape of the tumors. 

Abstract Glass properties are governed by the interplay between network formers and network modifiers; for a given composition of network formers, the ratio of different cationic modifiers compensating the anionic species in the network has a profound effect, which is often nonlinear, called a mixed modifier effect (MME). We have investigated the MME of sodium (Na) and calcium (Ca) in an aluminosilicate (NCAS) glass series following the formula [Na₂O]_30−x[CaO]_x[Al₂O₃]₁₀[SiO₂]₆₀, wherex = 0, 7.5, 15, 22.5, and 30. A nonadditive trend was observed in hardness and indentation toughness, with aqueous corrosion resistance exhibiting a shift from incongruent to congruent corrosion, whereas the network structure determined by molecular dynamics simulations revealed no significant trend with composition. Additionally, the NCAS glass containing both [Na₂O] and [CaO] within an intermediate range exhibited superior resistance to wear at high humidity, a clear MME phenomenon previously only observed in soda–lime silica. 

Abstract Germanate glasses are of particular interest for their excellent optical properties as well as their abnormal structural changes that appear with the addition of modifiers, giving rise to the so‐calledgermanate anomaly. This anomaly refers to the nonmonotonic compositional scaling of properties exhibited by alkali germanate glasses and has been studied with various spectroscopy techniques. However, it has been difficult to understand its atomic scale origin, especially since the germanium nucleus is not easily observed by nuclear magnetic resonance. To gain insights into the mechanisms of the germanate anomaly, we have constructed a structural model using statistical mechanics and topological constraint theory to provide an accurate prediction of alkali germanate glass properties. The temperature onsets for the rigid bond constraints are deduced from in situ Brillouin light scattering, and the number of constraints is shown to be accurately calculable using statistical methods. The alkali germanate model accurately captures the effect of the germanate anomaly on glass transition temperature, liquid fragility, and Young's modulus. We also reveal that compositional variations in the glass transition temperature and Young's modulus are governed by the O–Ge–O angular constraints, whereas the variations in fragility are governed by the Ge–O radial constraints.