CAREER: Coupling Adsorption and Mechanics: Towards the Development of Smart Porous Materials

气体和液体在多孔材料中的吸附在分离过程中起着重要作用。该项目的重点是了解流体吸附和吸附材料的机械性能之间的关系。具体而言，将开发新的理论方法来预测吸附材料在流体吸附过程中如何变形，以及这种机械响应如何影响多孔材料的吸附行为。解决这些悬而未决的问题将指导智能多孔材料的开发，推进膜分离过程和软机器人的设计，并有助于设计下一代传感设备。设计先进的吸附剂和膜材料的能力可能会大大提高分离技术的效率，最终改善清洁水的获取，并为碳固存提供新的战略。该项目的教育部分将包括开发和传播在线编程课程，以及与代表性不足群体的高中生开展外联活动。这个CAREER项目将产生一个具有定量预测能力的原子建模框架，以指导智能多孔材料的工程设计，并对流体吸附进行受控的机械响应。经典的分子模拟技术（蒙特卡罗和分子动力学模拟）和有限元建模将以互补的方式用于预测从原子到宏观尺度的材料响应。由此产生的计算框架将被应用于预测和发展的基本理解吸附引起的变形在三类技术相关的材料：沸石，薄的多孔二氧化硅薄膜，和多孔聚合物。从拟议的研究中出现的知识将改变这些多孔材料的应用，如具有受控渗透的膜，化学机械传感器和致动器的发展路径。该项目的教育部分将解决在线课程“Python for Chemical Engineering Calculations”的开发，这将有助于向全国的本科化学工程学生教授这种强大的编程语言。讲座视频和课程材料将免费提供给公众，并通过基于网络的LearnChemE资源传播。还将开发一套针对高中和社区大学学生的补充教育模块。调查员将在邻近的新泽西学校介绍这些模块。这些学校访问将有助于从事传统上代表性不足的群体从北方泽西岛的不同人口在干活动。这个奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

Adsorption of gases and liquids in porous materials plays a significant role in separation processes. This project focuses on understanding the relationship between fluid adsorption and the mechanical properties of adsorbent materials. Specifically, novel theoretical approaches will be developed to predict how adsorbent materials deform during fluid adsorption and how this mechanical response affects the adsorption behavior of porous materials. Addressing these open questions will guide the development of smart porous materials, advance membrane separations processes and the design of soft robotics, and aid in engineering the next-generation of sensing devices. The ability to design advanced adsorbent and membrane materials may dramatically improve the efficiency of separation technologies, ultimately improving access to clean water and providing new strategies for carbon sequestration. The educational component of the project will include development and dissemination of an online programming course, as well as outreach activities with high-school students from underrepresented groups. This CAREER project will result in an atomistic modeling framework with quantitative predictive capabilities to guide the engineering of smart porous materials with controlled mechanical responses to fluids adsorption. Classical molecular simulation techniques (Monte Carlo and molecular dynamics simulations) and finite element modeling will be used in a complementary fashion to predict materials responses from atomistic to macroscopic scales. The resulting computational framework will be applied to predict and develop fundamental understanding of adsorption-induced deformation in three classes of technologically relevant materials: zeolites, thin porous silica films, and porous polymers. The knowledge to emerge from the proposed research will transform the paths to development of these porous materials for applications such as membranes with controlled permeation, chemomechanical sensors, and actuators. The educational component of this project will tackle the development of an online course “Python for Chemical Engineering Calculations,” which will help teach this powerful programming language to undergraduate chemical engineering students across the nation. The lecture videos and course materials will be made freely available to the public and disseminated through the web-based LearnChemE resource. A complementary set of educational modules targeting high-school and community college students will also be developed. The investigator will present these modules at neighboring New Jersey schools. These school visits will serve to engage traditionally underrepresented groups from Northern Jersey’s diverse population in STEM activities.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.