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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Evaluation of GlassNet for physics-informed machine learning of glass stability and glass-forming ability

Glassy materials form the basis of many modern applications, including nuclear waste immobilization, touch-screen displays, and optical fibers, and also hold great potential for future medical and environmental applications. However, their structural complexity and large composition space make design and optimization challenging for certain applications. Of particular importance for glass processing and design is an estimate of a given composition's glass-forming ability (GFA). However, there remain many open questions regarding the underlying physical mechanisms of glass formation, especially in oxide glasses. It is apparent that a proxy for GFA would be highly useful in glass processing and design, but identifying such a surrogate property has proven itself to be difficult. While glass stability (GS) parameters have historically been used as a GFA surrogate, recent research has demonstrated that most of these parameters are not accurate predictors of the GFA of oxide glasses. Here, in this work, we explore the application of an open-source pre-trained neural network model, GlassNet, that can predict the characteristic temperatures necessary to compute GS with reasonable performance and assess the feasibility of using these physics-informed machine learning (PIML)-predicted GS parameters to estimate GFA. In doing so, we track the uncertainties at each step of the computation—from the original ML prediction errors to the compounding of errors during GS estimation, and finally to the final estimation of GFA. While GlassNet exhibits reasonable accuracy on all individual properties, we observe a large compounding of error in the combination of these individual predictions for the PIML prediction of GS, finding that random forest models offer similar accuracy to GlassNet. We also break down the performance of GlassNet on different glass families and find that the error in GS prediction is correlated with the error in crystallization peak temperature prediction. Lastly, we utilize this finding to assess the relationship between top-performing GS parameters and GFA for two ternary glass systems: sodium borosilicate and sodium iron phosphate glasses. We conclude that to obtain true ML predictive capability of GFA, significantly more data needs to be collected.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Predicting nepheline precipitation in waste glasses using ternary submixture model and machine learning

Nepheline precipitation in nuclear waste glasses during vitrification can be detrimental due to its negative effect on chemical durability. Developing models to accurately predict nepheline precipitation from compositions is important to increase waste loading since existing models can be overly conservative. In this study, an expanded dataset containing 955 glasses was compiled from literature data, where 355 glasses are for high-level waste (HLW). Previously developed submixture models were refitted using the new dataset, where a misclassification rate of 7.8% was achieved. Nine machine learning (ML) algorithms (e.g., k-nearest neighbor, Gaussian process regression, artificial neural network, support vector machine, decision tree, etc.) were applied to evaluate their ability of predicting nepheline precipitation from compositions. Model accuracy, precision, recall/sensitivity, and F1 score were systemically compared between different ML algorithms and modeling protocols. Good model prediction with an accuracy ~0.9 (misclassification rate of ~10%) was observed with different algorithms under certain protocol. This study evaluated various ML models to predict nepheline precipitations in waste glasses, highlighting the importance of data preparation, modeling protocol, and their effect on model stability and reproducibility. The results provide insights into applying ML to predict glass properties and suggest areas for future research on modeling nepheline precipitations.

Lu, Xiaonan↗
DOE OSTI2024

Structure–property relations of sodium iron phosphate nuclear waste glasses: Effects of iron redox ratio and glass composition

Iron phosphate glasses, known for their exceptional chemical durability and potential applicability in nuclear waste management, have gained significant attention over the years. The structures of these glasses are complicated by the coexistence of Fe 3+ and Fe 2+ , which plays a crucial role in determining their structures and properties. Here, this work uses molecular dynamics simulations to study the structural changes in Na 2 O–Fe 2 O 3 –P 2 O 5 glasses with varying glass composition and Fe 2+ /Fe 3+ redox ratio. It was found that the redox ratio and modifier contents significantly affected the short-range and medium-range orders in the glasses. Significant changes in the local environments around P 5+ and Fe 3+ were observed, as reflected by the bond distances and coordination numbers. Na + cations are found to preferentially associate with Fe 3+ (rather than Fe 2+ ), whereas Fe 2+ has stronger association with P 5+ than Na + , confirming the structural role of Fe 2+ as a glass modifier. The disruptions in P–O–P linkages upon increasing FeO suggest that FeO causes glass depolymerization. These glasses achieved higher connectivity with increasing Fe 3+ / (Fe 3+ + Fe 2+ ) ratios, conerting phosphorous Q 2 to Q 3 units and iron Q 5 units to Q 4 units. The decrease of nonbridging oxygen fractions with increasing Fe 3+ / (Fe 3+ + Fe 2+ ) ratios, through creating P–O–Fe linkages, is the main reason of enhanced network connectivity. Quantitative structure–property relationship analyses with different structural descriptors were used to correlate with measured properties. The analyses provided valuable insights into structure–property relationships, emphasizing the importance of choosing relevant energy parameters and defining glass network connectivity, particularly in F net descriptors. It was found the Fe–O–P linkage density exhibits strong correlations to measured dissolution rates, supporting the importance of these linkages in improving the chemical durability in iron phosphate glasses.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Durability testing of actual Hanford waste glasses and their non-radioactive simulant glasses

The low-activity waste (LAW) fraction of Hanford tank waste will be converted to glass at the Waste Treatment and Immobilization Plant (WTP) and disposed on the Hanford site. The chemical durability of LAW glasses has been researched for decades to satisfy contract requirements. Most LAW glass durability data has been generated on non-radioactive simulant glasses fabricated via crucible melts. These non-radioactive glasses were chosen due to safety and cost reasons with confidence that radioactive waste glasses would exhibit similar behavior. To reduce the risk of significant differences in laboratory test response data between WTP melter waste glass and simulant glass, Product Consistency Tests (PCT, i.e., ASTM C1285-21) and Environmental Protection Agency (EPA) 1313 durability tests were performed on actual and simulant LAW glasses fabricated using laboratory-scaled melters. Actual and simulant glass durability test results are presented and statistically compared. Finally, differences in test responses were found to be within experimental uncertainty.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

A Data-driven Approach for Predicting Nepheline Crystallization in High-Level Waste Glasses

High-level waste (HLW) glasses with high aluminum content are prone to nepheline crystallization during the slow canister cooling that is experienced during large-scale production. Due to its detrimental effects on glass durability, nepheline precipitation must be avoided; however, developing robust, predictive models for nepheline crystallization behavior in HLW glasses is difficult due to their compositional complexity. Using overly conservative constraints to predict nepheline formation can limit the waste loading to lower than the achievable capacity. In this study, a robust data-driven model using five compositional features has been developed to predict nepheline formation. The analysis of the model and the data show that there is an overlap, instead of a distinct compositional boundary, between glasses that form and do not form nepheline. As a result, the model’s predictive accuracy is not the same throughout the feature space and instead is dependent on the location of the glass composition in the dimensionally reduced feature space.

Sargin, Irmak↗
DOE OSTI2024

Glass Design Using Machine Learning Property Models with Prediction Uncertainties: Nuclear Waste Glass Formulation

The United States Department of Energy is responsible for managing the legacy nuclear waste stored in underground tanks at the Hanford Site. The waste will be separately vitrified as low-activity waste and high-level waste fractions. Waste glass formulation algorithms have been traditionally developed using partial quadratic mixture property-composition models. Recently, machine learning (ML) techniques have been used to predict glass properties and discover new glass materials for nuclear waste vitrification, and these advancements can be utilized to improve waste glass composition design. In this proof-of-principle study, ML algorithms such as Gaussian process regression (GPR) were used to interpolate glass properties (e.g., viscosity, electrical conductivity, chemical durability). After selecting appropriate sets of GPR hyper-parameters for each property, an optimization program was developed to formulate glass compositions to maximize waste loading while simultaneously satisfying property within constraints. The results of the ML-based waste loadings and glass compositions were compared to those obtained using the traditional methods. Comparing to the previous glass design framework, the ML-based optimization methods offer improved glass designs and a streamlined approach to generation of optimally designed data and near real-time updates.

glass formulation, machine learning, constraints, ↗
DOE OSTI2022

Effect of zeolite type, temperature, and pH on Stage III glass alteration behavior for two nuclear waste glasses

We report long-term laboratory scale static alteration tests (up to 1500 d) were conducted on two low-activity waste glasses (LAWA44 and IDF18-A161) at S/V = 2000 m –1 . Tests were seeded with either analcime, clinoptilolite, zeolite P1, or zeolite P2 to induce Stage III glass alteration behavior. The effect of several parameters on glass Stage III behavior was studied with consideration of several variables, including temperature (22 °C – 90 °C) and pH (floating or initially set using KOH at values from 9.5 to 11.5). Temporal solution data and solids characterization at the end of the experiments are reported. Solution data demonstrated the occurrence of a sustained rate acceleration at temperatures down to 40 °C and the activation energy for these Stage III rates was determined for both glasses. Three major secondary phases were identified at the end of the experiments for both glasses: zeolite P1, zeolite P2, and/or analcime (along with minor phases: zeolites, clays, carbonates). The identification of the crystalline phases at the end of the experiment often differed from the zeolite seed phase. In addition, the use of the Avrami equation showed unimpeded zeolite growth (3-dimensional) in certain conditions, suggesting that in certain conditions the glass dissolution rate is controlled by zeolite formation kinetics whereas in other conditions the glass corrosion process impeded zeolite formation kinetics. The morphological evolution of the samples revealed the growth of the zeolites which completely fill the intergranular space in the powder bed. These results are discussed with regards to previous seeded and unseeded Stage III dissolution rate experiments performed on low- and high-activity nuclear waste glasses.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

Nepheline crystallization and the residual glass composition: Understanding waste glass durability

The vast majority of High-Level Waste (HLW) originating from defense nuclear programs is sequestered and immobilized in borosilicate glass. Borosilicate glass is universally accepted for immobilizing HLW, but its efficiency has limitations based on the compositional makeup of the waste stream. The chemical durability of the glass is the most important factor in determining the longevity and usefulness of the final glass waste form. The primary detriment to this durability in glasses containing high levels of aluminum is nepheline (NaAlSiO 4 ) crystallization, as it is generally accompanied by a measurable decrease in the glass's chemical durability. This work seeks to understand nepheline crystallization, within the context of thermal history, and to elucidate the influence of compositional shifts in the residual glass (after crystallization) on the measured durability. The results presented within show a distinct deviation in leaching behavior as a function of structural makeup (calculated Q units). This understanding will provide practical information required for broadening glass compositional regions needed to more efficiently vitrify HLW.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Glass and a carbon‐free United States: What is glass's role in the upcoming green revolution?

Abstract The climate is changing rapidly due to anthropogenic greenhouse gases. Glass is a significant contributor to these emissions, with 95 million tons of CO 2 being emitted due to glass manufacturing in recent years. Furthermore, glass recycling remains poor in the United States, and most glass is left to landfill after a single use. The lack of recycling and the carbon footprint of manufacturing have made glass an environmentally damaging material despite its theoretical infinite recyclability. Additionally, glass has the potential to enable next‐generation energy materials, enabling the necessary technology for a green economy. In this review, we briefly cover the challenges of glass manufacturing and how to make glass the benchmark material for a carbon‐neutral economy.

Wilkinson, Collin↗
DOE OSTI2024

Effect of glass forming additives on low-activity waste feed conversion to glass

A significant effort was invested in the past to develop and refine mathematical models that relate the composition of nuclear waste glasses with their properties, such as viscosity, electrical conductivity, or chemical durability. However, less attention has been paid to the formulation of the melter feed itself, such as the chemical form and the particle size of the glass forming and modifying additives (GFMA), which have a significant effect on the feed-to-glass conversion process during melting. To address this issue, we systematically changed the mineral composition of a simulated low-activity waste melter feed and inspected its melting behavior. When substituting minerals with corresponding oxides and hydroxides, we found that different alumina sources (kyanite, gibbsite, boehmite, or corundum) had the strongest effect on the feed melting process, whereas the sources of Ca, Mg, and Zr had little effect. Further, the x-ray diffraction analysis showed that the alumina sources differ in their dissolution kinetics: early dissolving alumina sources, such as gibbsite (Al(OH) 3 ) and boehmite (AlO(OH)), increase the transient glass-forming melt viscosity at early stages, when gases still evolve, causing extended foaming, whereas alumina sources that dissolve at high temperatures, such as kyanite (Al 2 SiO 5 ) and corundum (Al 2 O 3 ), keep the transient glass-forming melt viscosity low and lead to a faster foam collapse. Using viscosity-composition relationship to estimate the viscosity of transient glass-forming melts in the primary foaming range, we found that the primary foam began to collapse at 360 to 800 Pa s, and fully collapsed between 65 and 260 Pa s. This result agrees with our previous studies, according to which the glass-forming melt viscosity at the cold cap foam bottom ranged from 24 to 85 Pa s.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2026

Statistical generic design of glass and optimization: Selective review on oxide glasses

Designing a single glass composition for a multidimensional property space is challenging, and the difficulty increases with the number of design criteria. Traditionally, the task is accomplished using multiple statistical models that describe the relationships between composition (C) and property (P) values, i.e., C-P models. Recently, the structure (S)-property (P) statistical modeling has emerged as a complementary approach. The S-P modeling approach has also been shown to be a preferred method for modeling glass properties, particularly when a small data set is available, such as in single-component studies, or when strong nonlinearities exist between composition and properties. The combined model package, C-S-P, implements the concept of generic glass design, i.e., designing glass for performance by first selecting a specific or optimized set of glass network structural groups using S-P models and then transferring the designed structures (genes) to a particular composition using C-S models. This article reviews a set of supporting cases from the previous C-S-P modeling studies of phosphate, silicate, and borosilicate glasses, which are relevant for many critical commercial applications. The methodology for developing the statistical C-S-P database is presented, enabling the application of P?S?C to achieve a generic glass design and optimization, targeting multiple design criteria for both performance and processing properties simultaneously.

Network structure↗
DOE OSTI2023

Evaluation Of Glass Density to Support the Estimation of Fissile Mass Loadings in Sludge Batch 10 Glasses

Per a directive from the Department of Energy Savannah River Operations Office (DOE-SR) in 2008, the fissile mass loading concentration must remain below 897 g/m 3 in each high-level waste (HLW) glass canister produced by the Defense Waste Processing Facility (DWPF). To support Sludge Batch 5 (SB5) processing, the Savannah River National Laboratory (SRNL) developed a technical basis that facilitates the evaluation of fissile mass loading of the glass product. The calculation is based on the iron (Fe) concentration in the glass as determined by measurements from the Slurry Mix Evaporator acceptability analysis as well as the glass density. In April 2022, a subsequent DOE-SR directive increased the fissile mass loading limit to 2500 g/m 3 beginning with Sludge Batch 11. Thus, the 897 g/m 3 limit still applies to Sludge Batch 10 (SB10) processing. For SB5 through initial Sludge Batch 9 (SB9) processing prior to coupled operation with the Salt Waste Processing Facility (SWPF), SRNL provided DWPF a bounding glass density value that was based on a statistical evaluation of density measurements. To eliminate the need for experimental work, a composition-based density model for HLW glasses was developed at SRNL in 2019. The objective of this report is to present the bounding glass density determined with the composition-based density model for SB10 sludge-only (SO) and coupled processing.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Information integrated glass module fabricated by integrated additive and subtractive manufacturing

In this Letter, we report a novel integrated additive and subtractive manufacturing (IASM) method to fabricate an information integrated glass module. After a certain number of glass layers are 3D printed and sintered by direct C O 2 laser irradiation, a microchannel will be fabricated on top of the printed glass by integrated picosecond laser, for intrinsic Fabry–Perot interferometer (IFPI) optical fiber sensor embedment. Then, the glass 3D printing process continues for the realization of bonding between optical fiber and printed glass. Temperature sensing up to 1000°C was demonstrated using the fabricated information integrated module. In addition, the long-term stability of the glass module at 1000°C was conducted. Enhanced sensor structure robustness and harsh temperature sensing capability make this glass module attractive for harsh environment structural health monitoring.

Zhang, Qi (ORCID:0000000268655981)↗
DOE OSTI2021

Evolution of glass structure during femtosecond laser assisted crystallization of LaBGeO 5 in glass

Single crystal LaBGeO 5 architecture formed in La 2 O 3 -B 2 O 3 -2GeO 2 glass serves as a model system for understanding of the transformation from glass to single crystal under fs laser irradiation. This transformation is highly sensitive to glass composition, raising the need to understand how the glass structure and composition evolve during crystallization. In this work, micro X-ray absorption spectroscopy and X-ray fluorescence mapping were used to study the glass structure and composition preceding LaBGeO5 crystallization in the region surrounding the single crystal growth front. The results show precipitation of germanium nanoparticles, as well as anisotropic segregation of Ge and La prior to LaBGeO5 crystallization, which are explained assuming electromigration in high fields within the plasma formed by the fs laser irradiation. However, these nanoparticles do not serve as sites for heterogeneous nucleation. Furthermore, the similarity of local structure around Ge in the starting glass and the ultimate crystal appears to enhance the crystallization process.

36 MATERIALS SCIENCE↗