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Metal-organic frameworks (MOFs), made from metal ions and organic linkers, are promising materials for drug delivery due to their porous morphology. These components significantly affect drug loading, but the wide variety of irons and linkers makes it challenging to systematically evaluate their drug loading capacities. Machine Learning (ML) provides predictive models for drug loading based on properties such as ion type, linker structure, and MOFs morphology (e.g. surface area). However, the accuracy of these models is affected by hyperparameters. To improve model performance, this work develops a genetic algorithm (GA)-based optimization approach to build ML models for predicting drug loading rates. Our results demonstrate the predictability and generalizability of this approach for estimating the drug-loading capacities of different material-drug combinations. 

Silver nanowires (AgNWs) are one kind of nanomaterials for various applications such as solar panel cells and biosensors. However, the morphology of AgNWs, particularly their length and diameter, plays a critical role in determining the efficiency of energy storage systems and the transmittance of biosensors. Thus, it is imperative to study synthesis strategy for morphology control. This study focuses on synthesizing AgNWs through the solvothermal approach and aims to understand the individual and combined effects of three nucleants, NaCl, Fe(NO3)3 and NaBr, on the morphology of AgNWs. Using a modified successive multistep growth (SMG) approach and fine-tuning the nucleant concentrations, this study synthesized AgNWs with controllable aspect ratios, while minimizing the presence of undesirable byproducts like nanoparticles. Our results demonstrated the successful synthesis of AgNWs with favorable morphologies, including lengths of approximately 180 µm and diameters of 40 nm, thus resulting in aspect ratios of 4500. In addition, to assess the quality of the synthesized AgNWs, this work developed computational tools that uses MATLAB to automate the analysis of scanning electron microscope (SEM) images for detecting silver nanoparticles. This automated approach provides a quantitative analysis tool for material characterization and holds the promise for long-term evaluation of diverse AgNW samples, thereby paving the way for advancements in their synthesis and application. Overall, this study demonstrates the significance of morphology control in AgNW synthesis and presents a robust framework for material characterization and quality analysis. 

Uncertainty is a common feature in first-principles models that are widely used in various engineering problems. Uncertainty quantification (UQ) has become an essential procedure to improve the accuracy and reliability of model predictions. Polynomial chaos expansion (PCE) has been used as an efficient approach for UQ by approximating uncertainty with orthogonal polynomial basis functions of standard distributions (e.g., normal) chosen from the Askey scheme. However, uncertainty in practice may not be represented well by standard distributions. In this case, the convergence rate and accuracy of the PCE-based UQ cannot be guaranteed. Further, when models involve non-polynomial forms, the PCE-based UQ can be computationally impractical in the presence of many parametric uncertainties. To address these issues, the Gram–Schmidt (GS) orthogonalization and generalized dimension reduction method (gDRM) are integrated with the PCE in this work to deal with many parametric uncertainties that follow arbitrary distributions. The performance of the proposed method is demonstrated with three benchmark cases including two chemical engineering problems in terms of UQ accuracy and computational efficiency by comparison with available algorithms (e.g., non-intrusive PCE).