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

合作研究：可视化远离平衡状态的胶体材料中的统计力网络

基本信息

批准号：
2104869
负责人：
Safa Jamali
金额：
$ 37.5万
依托单位：
Northeastern University
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-08-15 至 2024-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2104869&HistoricalAwards=false
关键词：
Collaborative Research Visualizing statistical force

项目摘要

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.

食品，消费产品，自然环境，生物系统和建筑材料中，我们周围到处都发现了液体中颗粒颗粒的非技术摘要。这些材料的物理和机械性能，其保质期及其功能受到粒子如何相互作用的重大影响。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尺寸，通常具有粗糙的边缘，而不是完全光滑和球形。了解如何处理和处理这种类型的胶体材料为我们的国家提供了重要的经济和技术优势。当胶体被迫以高度浓缩的浆液流动时，颗粒聚集并集体抵抗运动，从而大大增加了设备的压力和灾难性故障。该项目使用高级网络科学概念，实验和仿真来研究一系列流动场景中的这种类型的悬架。从这项工作中获得的见解将使利用密集的颗粒系统的众多学术研究人员和工业从业人员受益。与软物质物理有关的基本概念将通过社交媒体上的夏令营和公民科学广泛地传播给K-12学生和公众。此外，该项目产生的最先进的结果将纳入本科和研究生课程中，并在旨在吸引少数群体和代表性不足的科学家的讲习班中。技术抽象密集的颗粒物材料在许多制造业领域中都普遍存在，例如药品，消费者和食品以及能源行业，以及能源行业。了解流动密度悬浮液的多尺寸性质将推动新颖和上级材料的自下而上设计。该项目通过对负责从粒子级别到宏观量表的压力传播的力网络产生统计描述来提供密集悬浮液物理学的基本理解。中心假设是可以使用粒子摩擦和动力学调整承重网络中的时空特征。 PI将结合实验和模拟，以研究网络形态的性质和放松的性质，并在经历流动滞后，蠕变和流动迅速停止的胶体悬浮液中。实验涉及使用共聚焦流变学，这是一种高分辨率和高速技术，可以测量流动应力，同时直接对单个胶体的运动进行成像。实验观察结果将与计算机模拟相结合，这些模拟结合了粗糙表面之间的详细流体物理。这些技术可以在网络级别上分析集群，包括它们的发展方式和流动系统的变化。在稠密的悬架中，巨型网络被认为可以持续并控制整个系统的机制。当非理想的颗粒通过薄层流体，验证颗粒模型分离时，该项目将研究粒子网络，这些模型将中尺度的合作长度连接到流动性流动性，并利用胶体特性故意改变了导致意外流动特性的网络模式。这种奖项反映了NSF的法定任务和经过评估的范围。