Griffith Justin E, Chen Yusu, Liu Qingsong, Wang Qifeng, Richards Jeffrey J, Tullman-Ercek Danielle, Shull Kenneth R, Wang Muzhou
Department of Chemical and Biological Engineering, Northwestern University, Evanston, Illinois 60208, USA.
Department of Materials Science & Engineering, Northwestern University, Evanston, Illinois 60208, USA.
Mater Horiz. 2023 Jan 3;10(1):97-106. doi: 10.1039/d2mh01064j.
Machine learning approaches have introduced an urgent need for large datasets of materials properties. However, for mechanical properties, current high-throughput measurement methods typically require complex robotic instrumentation, with enormous capital costs that are inaccessible to most experimentalists. A quantitative high-throughput method using only common lab equipment and consumables with simple fabrication steps is long desired. Here, we present such a technique that can measure bulk mechanical properties in soft materials with a common laboratory centrifuge, multiwell plates, and microparticles. By applying a homogeneous force on the particles embedded inside samples in the multiwell plate using centrifugation, we show that our technique measures the fracture stress of gels with similar accuracy to a rheometer. However, our method has a throughput on the order of 10 samples per run and is generalizable to virtually all soft material systems. We hope that our method can expedite materials discovery and potentially inspire the future development of additional high-throughput characterization methods.
机器学习方法迫切需要大量材料特性数据集。然而，对于机械性能而言，当前的高通量测量方法通常需要复杂的机器人仪器，其巨大的资本成本让大多数实验人员望而却步。人们长期以来一直渴望有一种仅使用普通实验室设备和耗材且制造步骤简单的定量高通量方法。在此，我们展示了一种技术，该技术可以使用普通实验室离心机、多孔板和微粒来测量软材料的整体机械性能。通过离心对多孔板中样品内嵌入的颗粒施加均匀力，我们表明我们的技术测量凝胶断裂应力的精度与流变仪相当。然而，我们的方法每次运行的通量约为10个样品，并且几乎可以推广到所有软材料系统。我们希望我们的方法能够加快材料发现的进程，并有可能激发未来更多高通量表征方法的发展。
