Collaborative Research: Visualizing statistical force networks in colloidal materials far-from-equilibrium

液体中的颗粒悬浮液在我们周围的食品、消费品、自然环境、生物系统和建筑材料中随处可见。这些材料的物理和机械性能、保质期和功能都受到颗粒相互作用的严重影响。更好的材料设计需要了解粒子相互作用如何产生某些类型的机械行为。这些系统中的颗粒具有各种形状和大小，并且通常具有粗糙的边缘，而不是完全光滑和球形。了解如何处理和加工这种类型的胶体材料为我们的国家提供了显着的经济和技术优势。当胶体被迫在高浓度浆料中流动时，颗粒聚集并集体抵抗运动，导致压力大幅增加和设备的灾难性故障。该项目使用先进的网络科学概念，实验和模拟，在一系列流动场景中研究这种类型的堵塞悬浮液。从这项工作中获得的见解将有利于广泛的学术研究人员和工业从业人员，利用密集的颗粒系统。与软物质物理学相关的基本概念将通过夏令营和社交媒体上的公民科学广泛传播给K-12学生和公众。此外，国家的最先进的成果，从这个项目产生的将被纳入本科生和研究生课程，并在研讨会上，旨在从事少数民族和代表性不足的scientists.Technical AbstractDense颗粒材料是无处不在的许多制造领域，如制药，消费品和食品，能源行业。了解流动的稠密悬浮液的多尺度性质将促进新的和上级材料的自下而上的设计。该项目通过生成负责从粒子级到宏观尺度的应力传播的力网络的统计描述，提供了对稠密悬浮体物理学的基础性理解。核心假设是，承载网络中的时空签名可以使用粒子摩擦和动力学进行调整。PI将结合联合收割机的实验和模拟，研究网络形态的性质和松弛的胶体悬浮液经历流动滞后，蠕变，和快速停止流动。实验涉及使用共焦流变仪，这是一种高分辨率和高速的技术，测量流动应力，同时直接成像个别胶体的运动。实验观察将与计算机模拟相结合，将粗糙表面之间的详细流体物理学。这些技术使集群在网络层面的分析，包括它们如何演变和流动系统中的变化。在稠密的流动悬浮液中，巨大的网络被认为是持久的，并控制着整个系统的力学。该项目将研究非理想颗粒被薄层流体分离时的颗粒网络，验证将中尺度协同长度与流动流变学联系起来的颗粒模型，该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的评估被认为值得支持。影响审查标准。

Non-technical AbstractSuspensions of particles in liquids are found everywhere around us in foods, consumer products, natural settings, biological systems, and construction materials. The physical and mechanical properties of these materials, their shelf life, and their function are heavily influenced by how the particles interact with each other. Better design of materials requires an understanding of how particle interactions give rise to certain types of mechanical behavior. The particles in these systems come in all shapes and sizes, and often possess rough edges as opposed to being completely smooth and spherical. Understanding how to handle and process such types of colloidal materials provides significant economic and technological advantages to our nation. When colloids are forced to flow in highly concentrated slurries, the particles aggregate and collectively resist motion, leading to large increases in pressure and catastrophic failure in equipment. This project uses advanced network science concepts, experiments, and simulations in concert to study such types of jammed suspensions in a series of flow scenarios. The insight gained from this work will benefit a wide range of academic researchers and industrial practitioners that utilize dense particulate systems. Basic concepts related to soft matter physics will be disseminated broadly to K-12 students and the general public through summer camps and citizen science on social media. Moreover, state-of-the-art results generated from this project will be incorporated into undergraduate and graduate curriculum, and in workshops designed to engage minority and underrepresented scientists.Technical AbstractDense particulate materials are ubiquitous in many manufacturing fields, such as pharmaceuticals, consumer and food products, and the energy industry. Understanding the multiscale nature of flowing dense suspensions will advance the bottom-up design of novel and superior materials. This project provides foundational understanding in the physics of dense suspensions, by generating a statistical description of the force networks that are responsible for stress propagation from particle-level to macroscopic scale. The central hypothesis is that the spatiotemporal signatures in load-bearing networks can be tuned using particle friction and dynamics. The PIs will combine experiments and simulations to investigate the nature of network morphology and relaxation in colloidal suspensions undergoing flow hysteresis, creep, and rapid cessation of flow. Experiments involve the use of confocal rheometry, which is a high-resolution and high-speed technique that measures flow stresses while directly imaging the movement of individual colloids. The experimental observations will be combined with computer simulations that incorporate detailed fluid physics between roughened surfaces. These techniques enable the analysis of clusters at the network level, including how they evolve and change in flowing systems. In dense flowing suspensions, giant networks are thought to persist and control the mechanics of the entire system. This project will study particle networks when non-ideal particles are separated by thin layers of fluid, validate granular models that connect mesoscale cooperativity lengths to flow rheology, and utilize colloidal properties to deliberately change the network patterns responsible for unexpected flow properties.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.