Researchers are studying the optimization of passive micromixers, with a focus on how pillar configuration and gaps affect mixing efficiency. Different approaches are being explored, including the use of k-means priority clustering and physics-informed neural networks to analyze and improve micromixer performance. Some designs aim to achieve efficient mixing with low flow rates and sample consumption, and easy fabrication.