自动化系统加速多孔膜研发,提升实验效率与一致性。
Developing and Validating a High-Throughput Robotic System for the Accelerated Development of Porous Membranes
- 全自动化平台实现溶液制备、涂膜、浸入与压缩测试全流程
- 高通量实验验证聚合物浓度与湿度对膜性能的影响规律
- 适合材料研发、自驱动实验室及数据驱动优化场景
多孔聚合物膜的开发仍依赖大量人工试错,耗时且难复现。本研究构建了一套全自动膜制备与表征平台,基于非溶剂致相分离(NIPS)工艺,集成自动化溶液配制、刮刀涂膜、可控浸入及压缩测试。系统可精确调控聚合物浓度与环境湿度等参数,模块化设计支持并行处理与样本重复性操作,显著缩短实验时间并提升一致性。引入压缩测试作为敏感力学表征方法,通过应力-应变曲线自动分析估算膜的刚度,并作为孔隙率与样品内均匀性的代理指标。以聚砜为聚合物、PolarClean为绿色溶剂、水为非溶剂,开展验证实验,结果再现了聚合物浓度和环境湿度对膜性能的预期影响:浓度与湿度升高,膜刚度与均匀性提升,孔结构形态与力学响应随之变化。该平台支持高通量实验,可无缝接入自驱动实验室工作流,为基于数据驱动的多孔聚合物膜优化提供可扩展、可复现的基础。
原文摘要 · Abstract (English)
The development of porous polymeric membranes remains a labor-intensive process, often requiring extensive trial and error to identify optimal fabrication parameters. In this study, we present a fully automated platform for membrane fabrication and characterization via nonsolvent-induced phase separation (NIPS). The system integrates automated solution preparation, blade casting, controlled immersion, and compression testing, allowing precise control over fabrication parameters such as polymer concentration and ambient humidity. The modular design allows parallel processing and reproducible handling of samples, reducing experimental time and increasing consistency. Compression testing is introduced as a sensitive mechanical characterization method for estimating membrane stiffness and as a proxy to infer porosity and intra-sample uniformity through automated analysis of stress-strain curves. As a proof of concept to demonstrate the effectiveness of the system, NIPS was carried out with polysulfone, the green solvent PolarClean, and water as the polymer, solvent, and nonsolvent, respectively. Experiments conducted with the automated system reproduced expected effects of polymer concentration and ambient humidity on membrane properties, namely increased stiffness and uniformity with increasing polymer concentration and humidity variations in pore morphology and mechanical response. The developed automated platform supports high-throughput experimentation and is well-suited for integration into self-driving laboratory workflows, offering a scalable and reproducible foundation for data-driven optimization of porous polymeric membranes through NIPS.
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