Elucidating Molecular Design Principles for Copolymer Membranes with Solute-Tailored Selectivity for the Separations of Rare Earth Elements

稀土元素是现代电子设备和绿色能源技术的重要组成部分。例如，含有稀土的高磁通磁铁对硬盘驱动器、风力涡轮机和电动马达的运行至关重要。找出从国内矿藏中分离稀土或从过时电子产品中回收稀土的方法，对于维持这些材料的可靠供应至关重要。目前用于分离这些资源的溶剂萃取法消耗大量的化学试剂和能源，同时产生大量的废水。因此，传统的稀土分离过程很难持续实施。在许多其他应用中，膜分离在可持续性和能源效率方面显示出显著的优势。为了将这一范例转化为稀土分离，需要能够区分稀土离子的膜。然而，稀土离子在溶解时具有相似的大小和相同的电荷，这使得它们很难分离。这一多学科项目整合了膜科学、聚合物化学和数据科学领域的最新进展，以解决与界面和热力学现象相关的基本科学问题，这些现象允许稀土元素在聚合物膜上选择性传输。将进行系统的实验研究，以描述膜纳米结构、表面化学和稀土元素传输机制之间的关系。即将获得的基本知识对选择性膜的分子工程具有广泛的影响，这些膜解决了确保美国人民福祉和繁荣所需的其他关键分离挑战。例如，通过改变膜的纳米结构和化学成分，可以定制分子传输机制，使治疗药物的净化或饮用水的处理成为可能。该项目还通过培训下一批跨学科的科学家和工程师，帮助美国的分离科学格局发生革命性的变化。该计划的总体目标是设计能够可持续地分离稀土的新型膜系统。目前，对于能够分馏稀土离子的传输机制背后的界面和热力学现象还没有明确的认识。要解决这一关键的知识差距，就必须确定纳米结构和化学控制因素，这些因素支配着基于化学特性的膜渗透目标溶质的能力。因此，将追求以下具体目标，以建立溶质定制传输机制背后的现象的定量结构-性质关系。(1)合成后功能化的共聚物膜的制备和表征。这个多功能的材料平台提供了对膜纳米结构和化学的正交控制，因此可以询问各种界面和热力学现象。(2)开发一个统计学习框架，有效地在与共聚材料相关的巨大分子设计空间中导航。提出了基于模型的实验设计(MBDOE)和动态透析过滤实验，以识别主要的界面现象，速度比爱迪生搜索快100倍。(3)利用统计学习来指导共聚物膜中选择性传递机制的结构-性质关系的发展。这项研究计划提供了一个机会，在阐明膜能够基于化学因素而不是空间因素输送目标溶质的关键关系方面取得重大进展，其应用远远超出了稀土分离的范围。此外，拟议的工作提供了一种新的高通量范例，利用动态实验和MBDOE表征膜。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Rare-earth elements (REEs) are essential components in modern electronic devices and green energy technologies. For example, high flux magnets that contain REEs are critical to the operation of hard drives, wind turbines, and electric motors. Identifying methods to separate REEs from domestic ore deposits or recycle them from outdated electronics is critical to maintaining a reliable supply of these materials. The solvent extraction processes currently used to isolate these resources consume large amounts of chemical reagents and energy while producing large volumes of wastewaters. As such, traditional REE separation processes are difficult to implement sustainably. Membrane separations have demonstrated significant advantages in sustainability and energy efficiency in numerous other applications. To translate this paradigm to REE separations, membranes capable of distinguishing between REE ions are needed. However, REE ions have comparable sizes and the same charge when dissolved in solution, which makes them challenging to separate. This multidisciplinary project integrates recent advances in the fields of membrane science, polymer chemistry, and data science to address fundamental scientific questions related to the interfacial and thermodynamic phenomena that allow for the selective transport of REEs across polymer membranes. Systematic, experimental studies will be conducted to describe how membrane nanostructure, surface chemistry, and REE transport mechanisms are related. The fundamental knowledge to be gained has broad implications for the molecular engineering of selective membranes that address other critical separation challenges needed to ensure the well-being and prosperity of the American people. For example, by changing the membrane nanostructure and chemistry, molecular transport mechanisms can be tailored to enable the purification of therapeutic medicines or the treatment of drinking water. This project also helps revolutionize the separation science landscape of the U.S. by training the next cohort of interdisciplinary scientists and engineers.The overall goal of this proposal is to engineer novel membrane systems capable of separating REEs sustainably. Currently, there is no clear understanding of the interfacial and thermodynamic phenomena underlying the transport mechanisms that are capable of fractionating REE ions. Addressing this critical knowledge gap necessitates identifying the nanostructural and chemical control factors that govern the ability of membranes to permeate target solutes based on chemical identity. As such, the following specific aims will be pursued to establish quantitative structure-property relationships for the phenomena underlying solute-tailored transport mechanisms. (1) Fabricate and characterize copolymer membranes that are amenable to post-synthetic functionalization. This versatile materials platform offers orthogonal control over membrane nanostructure and chemistry such that a diverse array of interfacial and thermodynamic phenomena can be interrogated. (2) Develop a statistical learning framework to navigate the vast molecular design space associated with copolymer materials efficiently. Model-based design of experiments (MBDOE) and dynamic diafiltration experiments are proposed to identify the dominant interfacial phenomena up to 100 times faster than Edisonian searches. (3) Utilize statistical learning to guide the development of structure-property relationships for selective transport mechanisms in copolymer membranes. This research program presents an opportunity to make significant progress toward elucidating the critical relationships for membranes capable of transporting target solutes based on chemical, rather than steric, factors, having applications well beyond REE separations. Moreover, the proposed work offers a new, high-throughput paradigm to characterize membranes using dynamic experiments and MBDOE.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.