Sohrabi Majid, Razbin Milad
Department of Textile Engineering, Faculty of Engineering, University of Guilan, Rasht, Iran.
Department of Textile Engineering, Amirkabir University of Technology, Tehran, Iran.
Sci Rep. 2025 Jul 26;15(1):27306. doi: 10.1038/s41598-025-13159-0.
Nanofibers have gained recognition as promising materials for air filtration due to their high surface area-to-volume ratio, adjustable porosity, and exceptional mechanical properties. However, optimizing their structural characteristics to maximize filtration efficiency while minimizing pressure drop remains challenging due to the complexity of the electrospinning process. This study presents an artificial intelligence-based methodology to establish relationships between electrospinning parameters, nanofiber morphology, and filtration performance. An advanced statistical approach is used to systematically collect and analyze data, followed by modeling these relationships using artificial neural networks (ANN) and analytical formulas to enhance predictive accuracy. A genetic algorithm (GA) is subsequently utilized to refine electrospinning parameters, facilitating the production of nanofibers with enhanced filtration efficiency and optimized airflow resistance. The optimized nanofiber membranes are validated experimentally to assess their real-world performance. The findings demonstrate the potential of AI-driven design in fine-tuning nanofiber structures for advanced air filtration applications. The optimized sample achieved a filtration efficiency of 96%, a pressure drop of 110.23 Pa, and a quality factor of 0.0297. This study underscores the effectiveness of combining AI with electrospinning to develop high-performance air filtration materials.
由于具有高的比表面积、可调节的孔隙率和优异的机械性能，纳米纤维已成为空气过滤领域颇具前景的材料。然而，由于静电纺丝过程的复杂性，优化其结构特性以在使压降最小化的同时最大化过滤效率仍然具有挑战性。本研究提出了一种基于人工智能的方法，以建立静电纺丝参数、纳米纤维形态和过滤性能之间的关系。采用先进的统计方法系统地收集和分析数据，随后使用人工神经网络（ANN）和解析公式对这些关系进行建模，以提高预测准确性。随后利用遗传算法（GA）优化静电纺丝参数，促进生产具有更高过滤效率和优化气流阻力的纳米纤维。对优化后的纳米纤维膜进行实验验证，以评估其实际性能。研究结果表明，人工智能驱动的设计在微调纳米纤维结构以用于先进空气过滤应用方面具有潜力。优化后的样品实现了96%的过滤效率、110.23 Pa的压降和0.0297的品质因数。本研究强调了将人工智能与静电纺丝相结合以开发高性能空气过滤材料的有效性。
