Unfolding the elementary building blocks of dynamics and rheology of soft glassy materials

微凝胶是被溶解在其中的溶剂（如水）膨胀的聚合物网络。软固体是这种微凝胶的高浓度悬浮液，无毒，含水量高，生产成本低。因此，它们在药物输送、组织工程和伤口敷料等方面具有巨大的应用潜力。软固体的生产效率和最终使用性能（如保质期）取决于微观层面对机械性能及其流动行为的理解。该奖项支持联合计算，理论和实验研究，以提供对软固体悬浮液性质的基本理解。该奖项的结果将为设计具有目标性能的软固体提供工程工具。该奖项将为培养计算物理、工程和实验方面的研究生提供资源。这些教育活动将为来自代表性不足群体的本科生提供专业培训，使他们能够从事跨学科研究框架的工作。软颗粒玻璃是屈服应力流体，它被卡住，超出了等效硬球体的随机紧密排列。实验研究表明，这些悬浮液的流动行为可以通过调节颗粒间的粘附和排斥程度来控制。该奖项研究了单个颗粒水平的微观动力学与宏观特性之间的基本联系，并建立了一个有效的计算工具，用于预测具有不同颗粒间整体和界面相互作用的软颗粒玻璃的非线性流变特性。接触力、体积分数和流动强度对屈服、流动曲线和流动诱导的动力学非均质性的影响将被确定。建立剪切驱动的局部动力事件在低变形率和高变形率下对流动的作用及其与应力分布的联系。给出了具有动态非均质结构域的长度尺度与剪切速率之间的标度关系。将确定机械历史和残余应力对启动流动行为的影响。将开发机器学习模型，从静止悬架的微观结构预测屈服行为。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Microgels are polymer networks swollen by the solvent (e.g., water) in which they are dissolved. Soft solids are highly concentrated suspensions of such microgels which are nontoxic, have high water content, and enjoy a reasonably low cost of production. As such they have great potential for several applications in drug delivery, tissue engineering, and wound dressing. The production efficiency as well as the end-use properties (e.g., shelf-life) of soft solids depend on microscopic-level understanding of mechanical properties and their flow behavior. This award supports a joint computational, theoretical, and experimental investigation to provide a fundamental understanding of the properties of suspensions of soft solids. The outcome of this award will provide engineering tools to design soft solids with targeted properties. This award will provide resources to train graduate students in computational physics, engineering, and experimentation. The educational activities will provide professional training to undergraduate students from underrepresented groups to work on an interdisciplinary research framework. Soft particle glasses are yield stress fluids that are jammed beyond the random close packing of equivalent hard spheres. Experimental studies show that the flow behavior of these suspensions can be controlled by tuning the extent of interparticle adhesion and repulsion. This award investigates the fundamental connection between the microscopic dynamics at the individual particle level and the macroscopic properties and builds an efficient computational tool for predicting nonlinear rheological properties of soft particle glasses with different interparticle interactions in bulk and at the interface. The effect of contact forces, volume fractions, and flow strength on the yielding, flow curves, and flow-induced dynamical heterogeneities will be determined. The role of shear-driven localized dynamical events on the flow at low and high deformation rates and their connection with the stress distribution will be established. Scaling relationships between the length scale of the domain with dynamical heterogeneities and shear rate will be provided. The effect of mechanical history and residual stress on the start-up flow behavior will be determined. Machine learning models will be developed to predict the yield behavior from the microstructure of suspensions at rest.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.