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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 Fe3+ and Fe2+, which plays a crucial role in determining their structures and properties. This work uses molecular dynamics simulations to study the structural changes in Na2O–Fe2O3–P2O5 glasses with varying glass composition and Fe2+/Fe3+ 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 P5+ and Fe3+ were observed, as reflected by the bond distances and coordination numbers. Na+ cations are found to preferentially associate with Fe3+ (rather than Fe2+),  whereas Fe2+ has stronger association with P5+ than Na+, confirming the structural role of Fe2+ 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 Fe3+/(Fe3++Fe2+)${\mathrm{Fe}}^{3+}/({\mathrm{Fe}}^{3+}+{\mathrm{Fe}}^{2+})$ ratios, conerting phosphorous Q2 to Q3 units and iron Q5 units to Q4 units. The decrease of nonbridging oxygen fractions with increasing Fe3+/(Fe3++Fe2+)${\mathrm{Fe}}^{3+}/({\mathrm{Fe}}^{3+}+{\mathrm{Fe}}^{2+})$ 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 Fnet 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. [ABSTRACT FROM AUTHOR]

Purpose: To explore the association between progressive peripapillary capillary vessel density (pcVD) reduction and the progression of visual field (VF) impairment in individuals with normal tension glaucoma (NTG). Design: Prospective cohort study. Methods: The study enrolled 110 participants with one eye each, totalling 110 NTG eyes. VF defects were evaluated using standard automated perimetry mean deviation (MD), while pcVD measurements were obtained using optical coherence tomography angiography throughout the follow‐up period. Estimates of VF progression were determined by event‐based and trend‐based analyses. Fast VF progression was defined as an MD slope steeper than −0.5 dB/year, while the slow progression or stable VF was defined as an MD slope better or equal to −0.25 dB/year. Linear mixed‐effects models were employed to analyse the rates of change in pcVD reduction and VF MD decline over time. Additionally, univariable and multivariable linear models were used to examine the relationship between pcVD changes and VF loss rates in NTG. Results: Slow VF progression or stable VF was observed in 45% of subjects, while 25% had moderate progression and 30% showed fast progression. Patients with VF progression exhibited faster rate of pcVD reduction in peripapillary global region (−0.73 ± 0.40%/year vs. −0.56 ± 0.35%/year, p = 0.022). Moreover, this rate positively correlated with VF MD decline in NTG (estimate 0.278, 95% CI 0.122–0.433, p = 0.001). Conclusion: In individuals with NTG, faster VF progression was linked to a quicker reduction in pcVD, suggesting a positive correlation between pcVD decline and VF deterioration. [ABSTRACT FROM AUTHOR]

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, 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. [ABSTRACT FROM AUTHOR]

Glass is a versatile material with a remarkable history and many practical applications. It plays a critical role in our everyday lives, the advancement of science, and the development of many technologies. The Edisonian type trial‐and‐error method was commonly used for conventional design of glass compositions, which was time‐consuming and costly. With the urgent need to develop new glass compositions for technology applications rapidly, it has become necessary to develop precise property models with predictive powers using large databases and efficient formulation approaches. This paper reviews the design of glass compositions using these analytical and numerical models of composition–structure–property relations of glasses, some based on large databases and machine learning approaches. Aspects of data collection, model fitting, feature extraction, model evaluation, and uncertainty quantification will be covered. Furthermore, advances in the glass optimization framework and available tools are summarized with examples. The outlook and perspective for further glass property model development and formulation approaches are discussed. [ABSTRACT FROM AUTHOR]

Predicting the chemical durability of glass materials is important for various applications from daily life such as cell phone screens and kitchenware to advanced technologies such as nuclear waste disposal and biomedicine. In this work, we explored the prediction of the initial glass dissolution rates using structural features from molecular dynamics (MD) simulations for a series of glass compositions (total 28), including ZrO2‐ and V2O5‐containing boroaluminosilicate, borosilicate, and aluminosilicate glasses. The initial dissolution rates (r0) measured experimentally at 90°C with varying solution conditions were correlated with structural features (e.g., polyhedral linkages and non‐bridging oxygen species) obtained from MD simulations, either from this study or from literature. As hydrolysis of the glass network through breaking of the network former linkages (e.g., Si–O–Si, Si–O–Al, etc.) is a critical step of network glass dissolution, the statistics of these linkages obtained from MD were also correlated to r0 through linear regression, where the coefficients of determination (R2) and root mean square error are found to be 0.949 and 0.681, respectively. This model was compared and discussed with existing models developed by various approaches, including machine learning, the kinetic rate equation, topological constraint theory, and other descriptors from MD simulations. The discussion provides insights on future model improvements to predict glass dissolution. In addition, as the effect of V2O5 on glass dissolution was not well studied comparing to ZrO2, the impact of V2O5 was emphasized in this paper, suggesting that the impact is not the same across all glass compositions and test conditions. [ABSTRACT FROM AUTHOR]

Summary: For systems with uncertain linear models, bounded additive disturbances and state and control constraints, a robust model predictive control (MPC) algorithm incorporating online model adaptation is proposed. Sets of model parameters are identified online and employed in a robust tube MPC strategy with a nominal cost. The algorithm is shown to be recursively feasible and input‐to‐state stable. Computational tractability is ensured by using polytopic sets of fixed complexity to bound parameter sets and predicted states. Convex conditions for persistence of excitation are derived and are related to probabilistic rates of convergence and asymptotic bounds on parameter set estimates. We discuss how to balance conflicting requirements on control signals for achieving good tracking performance and parameter set estimate accuracy. Conditions for convergence of the estimated parameter set are discussed for the case of fixed complexity parameter set estimates, inexact disturbance bounds, and noisy measurements. [ABSTRACT FROM AUTHOR]

This paper proposes an effective fault management scheme for medium‐voltage direct current (MVDC) networks, which includes a virtual‐impedance‐based fault current limiter (VI‐FCL) on the DC side and a positive‐negative‐sequence (PNS) control scheme on the AC side. Depending on the fault current value, different protection strategies are adopted, which include either isolating the fault by opening circuit breakers or riding through the fault with the proposed VI‐FCL. It is known that unbalanced grid voltage can be found in conventional AC distribution feeders due to unbalanced loading conditions among three phases. The unbalanced grid voltage could induce double line‐frequency (2ω) ripples on both DC voltage and current, which could thereby trigger protection malfunction in the DC sub‐sections. Therefore, the PNS control scheme is used to eliminate the 2ω ripples of both DC voltages and currents caused by unbalanced AC voltages. Furthermore, a detailed 2ω mathematical model of the MVDC network under unbalanced AC voltage condition is derived to investigate how the 2ω ripple propagates across the network. A voltage source converter based multi‐terminal MVDC network is built to test DC faults with different fault currents, unbalanced AC voltages, and different control schemes. The test results verify both the proposed fault management scheme and the 2ω ripple propagation analysis. [ABSTRACT FROM AUTHOR]

Nepheline precipitation in nuclear waste glasses during vitrification can be detrimental due to the negative effect on chemical durability often associated with its formation. Developing models to accurately predict nepheline precipitation from compositions is important for increasing waste loading since existing models can be overly conservative. In this study, an expanded dataset of 955 glasses, including 352 high‐level waste glasses, was compiled from literature data. Previously developed submixture models were refitted using the new dataset, where a misclassification rate of 7.8% was achieved. In addition, nine machine learning (ML) algorithms (k‐nearest neighbor, Gaussian process regression, artificial neural network, support vector machine, decision tree, etc.) were applied to evaluate their ability to predict nepheline precipitation from glass compositions. Model accuracy, precision, recall/sensitivity, and F1 scores were systemically compared between different ML algorithms and modeling protocols. Model prediction with an accuracy of ~0.9 (misclassification rate of ~10%) was observed for different algorithms under certain protocols. This study evaluated various ML models to predict nepheline precipitation in waste glasses, highlighting the importance of data preparation and 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 precipitation. [ABSTRACT FROM AUTHOR]

Quantitative Structure Property Relationship (QSPR) analysis based on molecular dynamics (MD) simulations is a promising approach for establishing the composition‐property relationships of glasses and other materials with complex structures. A series of 20 borosilicate, aluminosilicate, and boroaluminosilicate glasses have been modeled using MD simulations with recently developed effective potentials. Short‐ and medium‐range structures of these glasses were analyzed and, based on the structural information, QSPR analysis of the initial dissolution rates (r0) of these glasses that were measured at 90°C and pH 9 by using various structural descriptors such as percentage of bridging oxygen species, network connectivity, and average ring size. The structural descriptor, Fnet, which contains both energetic information such as single bond strength and structural information such as cation coordination number and Qn distribution, was also used. It was found that while the overall network connectivity, average ring size and Fnet provide reasonable correlations with r0 of studied glasses, Fnet gives the best correlation among the descriptors. For glasses that show incongruent dissolution, it was found that modification of glass compositions to account for preferential release of modifier cations is necessary to achieve best correlations. The findings were discussed with results of recent studies on evaluating the compositional dependence of glass dissolution behavior using the topological‐constraints‐based models. [ABSTRACT FROM AUTHOR]

High‐alumina containing high‐level waste (HLW) will be vitrified at the Waste Treatment Plant at the Hanford Site. The resulting glasses, high in alumina, will have distinct composition‐structure‐property (C‐S‐P) relationships compared to previously studied HLW glasses. These C‐S‐P relationships determine the processability and product durability of glasses and therefore must be understood. The main purpose of this study is to understand the detailed structural changes caused by Al:Si and (Al + Na):Si substitutions in a simplified nuclear waste model glass (ISG, international simple glass) by combining experimental structural characterizations and molecular dynamics (MD) simulations. The structures of these two series of glasses were characterized by neutron total scattering and 27Al, 23Na, 29Si, and 11B solid‐state nuclear magnetic resonance (NMR) spectroscopy. Additionally, MD simulations were used to generate atomistic structural models of the borosilicate glasses and simulation results were validated by the experimental structural data. Short‐range (eg, bond distance, coordination number, etc) and medium‐range (eg, oxygen speciation, network connectivity, polyhedral linkages) structural features of the borosilicate glasses were systematically investigated as a function of the degree of substitution. The results show that bond distance and coordination number of the cation‐oxygen pairs are relatively insensitive to Al:Si and (Al + Na):Si substitutions with the exception of the B‐O pair. Additionally, the Al:Si substitution results in an increase in tri‐bridging oxygen species, whereas (Al + Na):Si substitution creates nonbridging oxygen species. Charge compensator preferences were found for Si‐[NBO] (Na+), [3]B‐[NBO] (Na+), [4]B (mostly Ca2+), [4]Al (nearly equally split Na+ and Ca2+), and [6]Zr (mostly Ca2+). The network former‐BO‐network former linkages preferences were also tabulated; Si‐O‐Al and Al‐O‐Al were preferred at the expense of lower Si‐O‐[3]B and [3]B‐O‐[3]B linkages. These results provide insights on the structural origins of property changes such as glass‐transition temperature caused by the substitutions, providing a basis for future improvements of theoretical and computer simulation models. [ABSTRACT FROM AUTHOR]

Understanding composition‐structure‐property relationships of high‐alumina nuclear waste glasses are important for vitrification of nuclear waste at the Hanford Site. Two series of glasses were designed, one with varying Al:Si ratios and the other with (Al + Na):Si ratios based on the international simple glass (ISG, a simplified nuclear waste model glass), with Al2O3 ranging from 0 to 23 mol% (0 to 32 wt%). The glasses were synthesized and characterized using electron probe microanalysis, X‐ray photoelectron spectroscopy, small angle X‐ray scattering, high‐temperature oxide melt solution calorimetry, and infrared spectroscopy. Glasses were crystal free, and the lowest Na2O and Al2O3 glass formed an immiscible glass phase. Evolution of various properties—glass‐transition temperature, percentage of 4‐coordinated B, enthalpy of glass formation—and infrared spectroscopy results indicate that structural effects differ based on the glass series. [ABSTRACT FROM AUTHOR]

Laser‐assisted material processing is a promising field for new material synthesis. This study explores the feasibility of using laser as a tool to synthesize SiO2‐Na2O‐CaO‐P2O5 bioactive glass coatings on Ti‐6Al‐4V alloy for bio‐implant application. Various laser processing conditions were explored to investigate their effects on coating characteristics. Detailed microstructure and elemental distribution on the top surface and in the cross‐sectional region of the bioactive glass‐coated alloy were investigated by scanning electron microscope and energy dispersive spectroscopy. Bio‐mineralization in simulated body fluid for both representative‐coated samples and uncoated control sample for comparison was carried out. The thermokinetic effects during laser processing were simulated using a multi‐physics finite element model. The results demonstrated synthesis of bioactive glass coating on the metal substrate with local segregation of Na, P, and Si elements and considerable dilution of Al and Ti in the coating from the alloy substrate impacted the bioactivity of the coating. Although the observations suggest that the laser processing is a promising method in applying bioactive glasses coatings, further efforts are warranted to optimize laser processing conditions to produce adherent coating while retaining its bioactivity. [ABSTRACT FROM AUTHOR]

Atom probe tomography ( APT) is a novel analytical microscopy method that provides three dimensional elemental mapping with sub-nanometer spatial resolution and has only recently been applied to insulating glass and ceramic samples. In this paper, we have studied the influence of the optical absorption in glass samples on APT characterization by introducing different transition metal optical dopants to a model borosilicate nuclear waste glass. A systematic comparison is presented of the glass optical properties and the resulting APT data quality in terms of compositional accuracy and the mass spectra quality for two APT systems: one with a green laser (532 nm, LEAP 3000X HR) and one with a UV laser (355 nm, LEAP 4000X HR). These data were also compared to the study of a more complex borosilicate glass ( SON68). The results show that the analysis data quality, particularly the compositional accuracy and sample yield, was clearly linked to optical absorption when using a green laser, while for the UV laser optical doping aided in improving data yield but did not have a significant effect on compositional accuracy. Comparisons of data between the LEAP systems suggest that the smaller laser spot size of the LEAP 4000X HR played a more critical role for optimum performance than the optical dopants themselves. The smaller spot size resulted in more accurate composition measurements due to a reduced background level independent of the material's optical properties. [ABSTRACT FROM AUTHOR]

Determination of trace levels of compounds in agri-foods are challenging due to the complexity of the agricultural and food matrices. A specific and complete separation and enrichment of the target compound is sometimes more important than the development of detection tools. Raman spectroscopy and its derivative, surface enhanced Raman spectroscopy (SERS), have been widely used for the detection of specific food components due to their unique ability to record 'fingerprinting' features of each molecule. However, Raman spectroscopy/SERS records the spectral signatures of all the food components, demonstrating that a pre-separation of the target compound is critical. Molecularly imprinted polymers (MIPs), defined as 'artificial antibodies', have been constructed and integrated with Raman spectroscopy/SERS for an accurate and reliable separation and detection of target compounds in agri-foods with minimum interference from food matrices. Compared to other separation elements (e.g., antibody, aptamer etc.) that can be integrated with Raman spectroscopy/SERS for sensing, MIPs do not contribute to spectral signature, can be reused multiple times and are more resistant to environmental factors, demonstrating the potential to be used for in-field and on-line screening of food safety and quality. [ABSTRACT FROM AUTHOR]

Numerous studies about sturgeon reproductive physiology over the past twenty years have helped researchers to understand the relationship between how sexual maturation in female sturgeon may affect the quality and yield of caviar. Here, works about white sturgeon ( Acipenser transmontanus) female reproductive physiology relating to egg harvest and roe quality are summarized. [ABSTRACT FROM AUTHOR]