Kumail Raza

The escalating demand for efficient particle separation in microfluidic systems necessitates innovative design solutions. This study presents a simulation-based topology optimization method to passively separate particles within a 2D microfluidic channel, eliminating the need for external forces. Leveraging a coupled Navier-Stokes solver and particle advection simulation, the framework iteratively refines the channel's geometry by minimizing an objective function quantifying particle mis-sorting. Our approach computationally generated optimal, manufacturable topologies, demonstrating a peak sorting efficiency of 0.6667 (66.67%) achieved by the second iteration, which then stabilized in subsequent iterations to 0.6111 (61.11%), significantly surpassing the adaptability and robustness of traditional, manually designed microfluidic channels. This work provides a robust, physics-based framework for exploring complex design spaces, representing a significant advancement in the development of high-performance, next-generation microfluidic devices.

The rapid global deployment of solar photovoltaic (PV) technology presents a significant and often overlooked challenge: the effective management of end-of-life (EoL) solar panels. This issue is particularly acute in developing and emerging economies, where established reverse logistics infrastructure is often lacking. A critical limitation in current academic literature is the oversimplified forecasting of EoL waste streams, which fails to account for the dynamic interplay of socio-economic, policy, and environmental variables. To bridge this gap, we propose a novel decision support system (DSS) for the design of a sustainable reverse logistics network. Our system uniquely integrates a hybrid, multi-factorial forecasting model combining a SARIMAX time series approach with a Gradient Boosting Regressor to provide a robust prediction of waste volume. The output of this predictive engine dynamically informs a multi objective, mixed integer linear programming (MILP) model, which optimizes the network design to simultaneously minimize economic costs and environmental impacts. Our findings demonstrate that this integrated framework offers a methodologically more realistic and adaptable approach for strategic planning than existing models, as validated through simulated scenarios. Our study provides an actionable blueprint for policymakers and industry leaders to proactively build a resilient and circular economy for a sustainable energy future.