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.
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Diabetes and post-transplant survival have been linked. However, the impact on post-transplant survival of patients supported on Continuous Flow (CF) axial left ventricular assist devices (LVAD) as a bridge to transplant (BTT) with diabetes has not been widely studied. This study attempts to assess the impact of diabetes type II (DM type II) as a comorbidity influencing survival patterns in the post-cardiac transplant population supported on LVADs and to test if the presence of a pre– transplant durable LVAD acts as an independent risk factor in long-term post-transplant survival. The UNOS database population from 2004 to 2015 was used to construct the cohorts. A total of 21,032 were transplanted during this period. The transplant data were further queried to extract CF-axial flow pumps BTT (HMII-BTT) patients and patients who did not have VAD support before the transplant. A total of 4224 transplant recipients had HMII at the time of transplant, and 13,131 did not have VAD support. Propensity analysis was performed, and 4107 recipients of similar patient characteristics to those in the BTT group were selected for comparison. The patients with a VAD had significantly reduced survival at 2 years post-transplant ( p = 0.00514) but this trend did not persist at 5 years ( p = 0.0617) and 10 years post-transplant ( p = 0.183). Patients with diabetes and a VAD significantly decreased survival at 2 years ( p = 0.00204), 5 years ( p = 0.00029), and 10 years ( p = 0.00193). The presence of a durable LVAD is not an independent risk factor for long-term survival. Diabetes has a longstanding effect on the posttransplant survival of BTT patients.
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null (Ed.)Uncertainty quantification (UQ) is an important part of mathematical modeling and simulations, which quantifies the impact of parametric uncertainty on model predictions. This paper presents an efficient approach for polynomial chaos expansion (PCE) based UQ method in biological systems. For PCE, the key step is the stochastic Galerkin (SG) projection, which yields a family of deterministic models of PCE coefficients to describe the original stochastic system. When dealing with systems that involve nonpolynomial terms and many uncertainties, the SG-based PCE is computationally prohibitive because it often involves high-dimensional integrals. To address this, a generalized dimension reduction method (gDRM) is coupled with quadrature rules to convert a high-dimensional integral in the SG into a few lower dimensional ones that can be rapidly solved. The performance of the algorithm is validated with two examples describing the dynamic behavior of cells. Compared to other UQ techniques (e.g., nonintrusive PCE), the results show the potential of the algorithm to tackle UQ in more complicated biological systems.more » « less