Collaborative Research: Decoding and encoding mechanistic relations between structure and function in crack resistance of articular cartilage and cartilage inspired biomaterials.

合作研究：解码和编码关节软骨和软骨启发生物材料的抗裂结构和功能之间的机械关系。

• 批准号: 1808026
• 负责人: Moumita Das
• 金额: $ 25万
• 依托单位: Rochester Institute of Tech
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-07-15 至 2022-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1808026&HistoricalAwards=false
• 关键词: Collaborative; Research; Decoding; encoding; mechanistic

Non-Technical SummaryArticular cartilage is a soft tissue which provides a smooth cushion and distributes mechanical load in joints. As a material, articular cartilage is remarkable. It is only a few millimeters thick, can routinely bear up to ten times one's body weight over 100-200 million loading cycles, and still avoids fracturing. The simultaneous strength, fracture resistance (toughness), and longevity of native articular cartilage remains unmatched in synthetic materials. Such properties are desperately needed for tissue engineering, tissue repair, and even soft robotics applications. The molecular mechanism underlying this exceptional toughness, however, is not well understood. This project will obtain an understanding of the underlying principles and mechanisms that lead to the toughness of articular cartilage, and provide criteria, as we do for cracks in airplane wings, for predicting the probability that initially untreated tears in cartilage will fracture further. The PIs will test the hypothesis that cartilage has such terrific properties due to the fact that it is comprised of two interweaving polymer networks, one which provides mechanical rigidity and one that provides dissipation. Moreover, this double network changes in composition with location in the tissue. These ideas will be tested using numerical simulation and comparison with experimental measurements of the tissue mechanical properties. Using this integrated approach, the PIs will elucidate mechanical structure-function relations underlying fracture toughness of articular cartilage (AC) which will lead to better predictions of cartilage mechanics and failure, and guide the design of new bioinspired materials. The project will provide insights into tissue failure, tissue repair therapies, and design principles for soft robotics. PIs will educate and train a new generation of scientists who understand physics, engineering, and biology, organize workshops aimed at teaching communication skills to graduate students, and promote diversity in STEM workforce. Technical SummaryArticular Cartilage (AC) is a soft tissue that covers the ends of bones to distribute mechanical load in joints. AC contains relatively few cells and its network-like extracellular matrix primarily determines its mechanical response. Its strength, toughness, and crack resistance are extremely high compared to synthetic materials, but the molecular mechanism underlying this exceptional toughness is not well understood. Given the heterogeneous, depth dependent, and multi-component structure and composition of AC, existing continuum descriptions are too coarse-grained to fully describe its fracture mechanics. The PIs will address this challenge by approaching cartilage fracture with a new structure function framework that combines rigidity percolation theory and microscale double-network hydrogel models, together with new confocal elastography experiments that can inform and interface with the model development. Using this integrated approach consisting of multi-scale mathematical modeling and state-of-the art experiments, they will test the hypothesis that the toughness of AC arises because (i) the reinforcing network state is in proximity to a mechanical phase transition allowing tunable mechanical response, and (ii) the tissue is a multi-component heterogeneous composite enabling novel response to stress and blunting of cracks. The project will obtain an understanding of the dependence of cracks on structure and composition of cartilage and similar soft tissues, as well as on loading conditions, and provide insights into tissue failure, and tissue repair therapies. More broadly, this new framework will enable novel and concrete predictions on how these structure, composition, and constitutive mechanical properties can be tuned to resist, and blunt cracks in biomimetic and engineered materials. PIs will educate and train a new generation of scientists who understand physics, engineering, and biology, and promote diversity in STEM workforce. Cohen and Bonassar will develop soft-skills curriculum units for graduate students and postdocs based on a recent science communication workshop held at Cornell by the Alan Alda Center for Communicating Science. Das will mentor minority and 1st generation students via RIT's McNair Program.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.

非技术摘要软骨是一种软组织，可提供光滑的垫子并在接头中分布机械载荷。作为一种材料，关节软骨是显着的。它的厚度只有几毫米，通常可以承受超过100-2亿个装载周期的十倍，并且仍然避免破裂。在合成材料中，天然关节软骨的同时强度，抗骨折（韧性）和寿命仍然无与伦比。这些特性迫切需要组织工程，组织修复甚至软机器人的应用。然而，这种特殊韧性的基本机制尚不清楚。该项目将了解导致关节软骨的韧性的基本原理和机制，并像我们对飞机翅膀的裂纹一样提供标准，以预测最初未经处理的软骨中未经处理的泪水会进一步裂缝的概率。 PI将检验以下假设：软骨具有如此出色的特性，因为它由两个相互交织的聚合物网络组成，该网络提供了机械刚度，并且提供了耗散。此外，这种双重网络在组成中随着组织中的位置而变化。这些想法将通过数值模拟和与组织机械性能的实验测量进行比较来测试。使用这种综合方法，PI将阐明关节软骨（AC）裂缝韧性的机械结构功能关系关系，这将更好地预测软骨力学和失败，并指导新的生物启发材料的设计。该项目将提供有关组织衰竭，组织修复疗法以及软机器人技术的设计原理的见解。 PI将教育和培训新一代的科学家，他们了解物理，工程和生物学，组织旨在向研究生讲授沟通技巧的研讨会，并促进STEM劳动力的多样性。技术摘要软骨（AC）是一种软组织，覆盖骨头的末端以分布关节的机械负荷。 AC包含相对较少的细胞，其网络样细胞外基质主要决定其机械响应。与合成材料相比，它的强度，韧性和抗裂纹性非常高，但是这种特殊韧性的分子机制尚不清楚。鉴于AC的异质性，深度依赖性和多组分结构和组成，现有的连续描述太粗糙了，无法完全描述其断裂力学。 PI将通过使用新的结构功能框架来解决软骨断裂，该框架结合了刚性渗透理论和微观的双NETWORK水凝胶模型，以及可以为模型开发提供信息和接口的新共聚焦弹性胶凝实验。使用由多尺度数学建模和最先进的实验组成的集成方法，它们将检验以下假设：AC的韧性出现了，因为（i）加强网络状态与机械相位易于调谐机械响应接近，并且（ii）组织是多组件的杂物化响应，对构图的新颖响应和blun sembers neks and ablun and blun and blun and blun neks blun neks blun blun。该项目将了解裂缝对软骨和类似软组织的结构和组成的依赖性以及对加载条件的依赖，并提供对组织衰竭和组织修复疗法的见解。更广泛地说，这个新框架将对这些结构，组成和本构的机械性能如何进行抵抗以及仿生和工程材料中的钝性裂缝进行新颖和具体的预测。 PI将教育和培训新一代的科学家，他们了解物理，工程和生物学，并促进STEM劳动力的多样性。 Cohen和Bonassar将根据艾伦·阿尔达（Alan Alda）传播科学中心在康奈尔（Cornell）举行的最近在康奈尔（Cornell）举行的科学传播研讨会，为研究生和博士后开发软技能课程单元。 DAS将通过RIT的McNair计划指导少数民族和第一代学生。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响审查标准，被认为值得通过评估来获得支持。

项目成果

Introduction to Active Matter

• DOI: 10.1039/d0sm90137g
• 发表时间: 2020-08-21
• 期刊: SOFT MATTER
• 影响因子: 3.4
• 作者: Das, Moumita;Schmidt, Christoph F.;Murrell, Michael
• 通讯作者: Murrell, Michael

Reentrant rigidity percolation in structurally correlated filamentous networks

结构相关丝状网络中的可重入刚性渗透

• DOI: 10.1103/physrevresearch.4.043152
• 发表时间: 2022
• 期刊: Physical Review Research
• 影响因子: 4.2
• 作者: Michel, Jonathan;von Kessel, Gabriel;Jackson, Thomas Wyse;Bonassar, Lawrence J.;Cohen, Itai;Das, Moumita
• 通讯作者: Das, Moumita

Rigidity and fracture of biopolymer double networks

生物聚合物双网络的刚性和断裂

• DOI: 10.1039/d1sm00802a
• 发表时间: 2022
• 期刊: Soft Matter
• 影响因子: 3.4
• 作者: Lwin, Pancy;Sindermann, Andrew;Sutter, Leo;Wyse Jackson, Thomas;Bonassar, Lawrence;Cohen, Itai;Das, Moumita
• 通讯作者: Das, Moumita