Sensor-Enabled Accelerated Engineering of Soft Materials

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Virginia Tech


Many grand societal challenges are rooted in the need for new materials, such as those related to energy, health, and the environment. However, the traditional way of discovering new materials is basically trial and error. This time-consuming and expensive method can't meet the quickly growing requirements for material discovery. To meet this challenge, the government of the United States started the Materials Genome Initiative (MGI) in 2011. MGI aims at accelerating the pace and reducing the cost of discovering new materials. The success of MGI needs materials innovation infrastructure including data tools, computation tools, and experiment tools. The last decade has witnessed significant progress for MGI, especially with respect to hard materials. However, relatively less attention has been paid to soft materials. One important reason is the lack of experimental tools, especially characterization tools for high-throughput experimentation. This dissertation aims to enrich the toolbox by trying new sensor tools for high-throughput characterization of hydrogels. Piezoelectric-excited millimeter-sized cantilever (PEMC) sensors were used in this dissertation to characterize hydrogels. Their capability to investigate hydrogels was first demonstrated by monitoring the synthesis and stimuli-response of composite hydrogels. The PEMC sensors enabled in-situ study of how the manufacturing process, i.e. bulk vs. layer-by-layer, affects the structure and properties of hydrogels. Afterwards, the PEMC sensors were integrated with robots to develop a method of high-throughput experimentation. Various hydrogels were formulated in a well-plate format and characterized by the sensor tools in an automated manner. High-throughput characterization, especially multi-property characterization enabled optimizing the formulation to achieve tradeoff between different properties. Finally, the sensor-based high-throughput experimentation was combined with active learning for accelerated material discovery. A collaborative learning was used to guide the high-throughput formulation and characterization of hydrogels, which demonstrated rapid discovery of mechanically optimized composite glycogels. Through this dissertation, we hope to provide a new tool for high-throughput characterization of soft materials to accelerate the discovery and optimization of materials.



Materials Genome Initiative, High-throughput experimentation, Sensing, Hydrogels, Active learning, Bayesian optimization