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The addition of V2O5 has been long known to increase the sulfur (as SO42-) solubility in borosilicate glasses. However, the mechanism governing this effect is still unknown. Although several studies have been published in the past two decades attempting to decipher the structural origins of increasing sulfur solubility as a function of V2O5 in borosilicate glasses, most of these studies remain inconclusive. The work presented in this paper attempts to answer the question, “Why does V2O5 increase sulfur solubility in borosilicate glasses?” Accordingly, a series of melt-quenched glasses in the system [30 Na2O – 5 Al2O3 – 15 B2O3 –50 SiO2](100-x) – xV2O5, where x varies between 0 – 9 mol.%, have been characterized for their short-to-intermediate range structure and the redox chemistry of vanadium using 11B, 27Al, 51V MAS NMR, Raman, and XPS spectroscopy. The impact of V2O5 on sulfur solubility in glasses has been followed by ICP-OES. The addition of ≤ 5 mol.% V2O5 results in a linear increase in sulfur solubility in the investigated glass system. Based on the results, we hypothesize that adding vanadium to the glasses increases their network connectivity, but reduces the network rigidity by replacing stronger Si–O–Si linkages with weaker Si–O–V linkages and forming (VO3)n-single chains. These modifications to the glass structure increase the flexibility of the network, thus making it possible to accommodate SO42− in their voids/open spaces. 

Limestone calcined clay cement (LC3) is a sustainable alternative to ordinary Portland cement, capable of reducing the binder’s carbon footprint by 40% while satisfying all key performance metrics. The inherent compositional heterogeneity in select components of LC3, combined with their convoluted chemical interactions, poses challenges to conventional analytical models when predicting mechanical properties. Although some studies have employed machine learning (ML) to predict the mechanical properties of LC3, many have overlooked the pivotal role of feature selection. Proper feature selection not only refines and simplifies the structure of ML models but also enhances these models’ prediction performance and interpretability. This research harnesses the power of the random forest (RF) model to predict the compressive strength of LC3. Three feature reduction methods—Pearson correlation, SHapley Additive exPlanations, and variable importance—are employed to analyze the influence of LC3 components and mixture design on compressive strength. Practical guidelines for utilizing these methods on cementitious materials are elucidated. Through the rigorous screening of insignificant variables from the database, the RF model conserves computational resources while also producing high-fidelity predictions. Additionally, a feature enhancement method is utilized, consolidating numerous input variables into a singular feature while feeding the RF model with richer information, resulting in a substantial improvement in prediction accuracy. Overall, this study provides a novel pathway to apply ML to LC3, emphasizing the need to tailor ML models to cement chemistry rather than employing them generically.