Rapid, autonomous high-throughput characterization of hydrogel rheological properties via automated sensing and physics-guided machine learning

摘要: High-throughput characterization (HTC) of composition-process-structure-property relations is essential for accelerating molecular and material discovery and manufacturing paradigms. Here, we present a rapid, autonomous method for HTC of hydrogel rheological properties in well plate formats via automated sensing and physics-guided supervised machine learning. The novel HTC method facilitates rapid, autonomous characterization of hydrogel rheological properties and percolation processes associated with gelation and network interpenetration in 96-well plate formats at a rate of 24 s/sample (70 times faster than the state-of-the-art). Viscoelastic properties and phase behavior obtained by the method were benchmarked against traditional rheology studies. The speed and utility of the method were demonstrated by high-resolution characterization of the gel point of Pluronic F127, collagen, and alginate …