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

多孔材料中气体和液体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与吸附材料机械性能之间的关系。具体来说，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，推进膜分离工艺和软机器人的设计，并有助于设计下一代传感设备。设计先进吸附剂和膜材料的能力可以显着提高分离技术的效率，最终改善清洁水的获取并提供碳封存的新策略。该项目的教育部分将包括开发和传播在线编程课程，以及针对代表性不足群体的高中生的外展活动。该职业项目将产生一个具有定量预测能力的原子建模框架，以指导对流体吸附具有受控机械响应的智能多孔材料的工程设计。经典分子模拟技术（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用于预测和发展对三类技术相关材料中吸附引起的变形的基本理解：沸石、多孔二氧化硅薄膜和多孔聚合物。拟议研究中得出的知识将改变这些多孔材料的开发路径，用于渗透受控膜、化学机械传感器和执行器等应用。该项目的教育部分将开发在线课程“Python 用于化学工程计算”，这将有助于向全国化学工程本科生教授这种强大的编程语言。讲座视频和课程材料将免费向公众开放，并通过基于网络的 LearnChemE 资源进行传播。还将开发一套针对高中生和社区学院学生的补充教育模块。研究人员将在新泽西州邻近的学校展示这些模块。这些学校访问将有助于让北泽西不同人群中传统上代表性不足的群体参与 STEM 活动。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影响审查标准进行评估，被认为值得支持。

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.