Engineering topics

Zong, Meirong

Publications and source records attributed to Zong, Meirong.

3 records

DOE OSTI2024

Understanding Trace Iron and Chromium Incorporation During Gibbsite Crystallization and Effects on Mineral Dissolution

Incorporation of pollutants, e.g., heavy metals, or critical elements, e.g., lithium, as impurities in mineral phases can significantly affect their mobility or sequestration in the environment. Even when present at low concentrations, impurities can alter the solubility and reactivity of the host mineral. Here, in this study, we investigate the incorporation of trace amounts of iron (Fe 3+ ) and chromium (Cr 3+ ) during the crystal growth of the aluminum (Al 3+ ) hydroxide, gibbsite, a major component of bauxite ores, an important soil mineral, and a dominant mineral phase in stored radioactive wastes. Using a comprehensive suite of analytical techniques, we show that both Cr 3+ and Fe 3+ can be incorporated into the gibbsite lattice during coprecipitation by replacing Al 3+ in octahedral sites. These small amounts are consistent with limited to no structural isomorphism shared between Al 3+ and Cr 3+ /Fe 3+ hydroxide precipitates, nor room temperature miscibility of their isostructural M 2 O 3 oxide forms, in contrast with oxyhydroxide forms where Al 3+ and Fe 3+ share similar structural topologies. Despite the limited uptake of Cr 3+ /Fe 3+ , we show that these impurities have significant implications for gibbsite dissolution behavior. The limited uptake of Cr 3+ /Fe 3+ (e.g. 0.43% Cr 3+ and 0.4% Fe 3+ ), we show that these impurities have significant implications for gibbsite dissolution behavior and subsequent reactivity in complex environments.

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DOE OSTI2023

Crystal dissolution by particle detachment

Crystal dissolution, which is a fundamental process in both natural and technological settings, has been predominately viewed as a process of ion-by-ion detachment into a surrounding solvent. Here we report a mechanism of dissolution by particle detachment (DPD) that dominates in mesocrystals formed via crystallization by particle attachment (CPA). Using liquid phase electron microscopy to directly observe dissolution of hematite crystals — both compact rhombohedra and mesocrystals of coaligned nanoparticles — we find that the mesocrystals evolve into branched structures, which disintegrate as individual sub-particles detach. The resulting dissolution rates far exceed those for equivalent masses of compact single crystals. Applying a numerical generalization of the Gibbs-Thomson effect, we show that the physical drivers of DPD are curvature and strain inherently tied to the original CPA process. Based on the generality of the model, we anticipate that DPD is widespread for both natural minerals and synthetic crystals formed via CPA.

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DOE OSTI2023

Machine learning assisted phase and size-controlled synthesis of iron oxide particles

Synthesis of iron oxides with specific phases and particle sizes is a crucial challenge in various fields, including materials science, energy storage, biomedical applications, environmental science, and earth science. However, despite significant advances in this area, much of the current palette of particle outcomes has been based on time-consuming trial-and-error exploration of synthesis conditions. The present study was designed to explore a very different approach to 1) predict the outcome of synthesis from specified reaction parameters based on using machine learning (ML) techniques, and 2) correlate sets of parameters to obtain products with desired outcomes by a newly designed recommendation algorithm. To achieve this, four ML algorithms were tested, namely random forest, logistic regression, support vector machine, and k-nearest neighbor. Among the models, random forest outperformed the others, attaining 96% and 81% accuracy when predicting the phase and size of iron oxide particles in the test dataset. Surprisingly, the permutation feature importance analysis revealed that volume, which may strongly relate to pressure, was one of the important features, along with precursor concentration, pH, temperature, and time, influencing the phase and size of iron oxide particles during synthesis. To verify the robustness of the random forest models, prediction and experimental results were compared based on 24 randomly generated methods in additive and non-additive systems not included in the datasets. The predictions of product phase and particle size from the models agreed well with the experimental results. Furthermore, a searching and ranking algorithm was developed to recommend potential synthesis parameters for obtaining iron oxide products with the desired phase and particle size from previous studies in the dataset. Furthermore, this study lays the foundation for a closed-loop approach in materials synthesis and preparation, beginning with suggesting potential reaction parameters from the dataset and predicting potential outcomes, followed by conducting experiments and analyses, and ultimately enriching the dataset.

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