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，含有稀土元素的高通量磁铁对硬盘驱动器、风力涡轮机和电动机的运行至关重要。确定从国内矿床中分离稀土元素或从过时的电子产品中回收稀土元素的方法，对于保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工艺消耗大量化学试剂和能量，同时产生大量废水。因此，传统的REE分离工艺难以可持续地实施。膜分离在许多其他应用中已显示出可持续性和能源效率方面的显著优势。为了将这种模式转化为REE分离，需要能够区分REE离子的膜。然而，当溶解在溶液中时，稀土离子具有相当的尺寸和相同的电荷，这使得它们难以分离。这个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，这些现象允许选择性运输稀土元素穿过聚合物膜。将进行系统的实验研究，以描述膜纳米结构，表面化学和稀土运输机制是如何相关的。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的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性质，可以定制分子运输机制，以实现治疗药物的纯化或饮用水的处理。该项目还通过培训下一批跨学科科学家和工程师来帮助彻底改变美国的分离科学景观。该提案的总体目标是设计能够可持续分离稀土的新型膜系统。目前，有没有明确的理解的界面和热力学现象的基础上的运输机制，能够分馏稀土离子。解决这一关键的知识差距需要确定纳米结构和化学控制因素，管理膜的能力，渗透目标溶质的化学身份的基础上。因此，将追求以下具体目标，以建立定量结构-性质关系的现象背后的溶质量身定制的运输机制。(1)制造和表征可进行合成后官能化的共聚物膜。这种多功能材料平台提供了对膜纳米结构和化学的正交控制，从而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，有效地导航与共聚物材料相关的巨大分子设计空间。基于模型的实验设计（MBDOE）和动态渗滤实验，提出了确定占主导地位的界面现象高达100倍的速度比爱迪生的搜索。(3)利用统计学习来指导共聚物膜中选择性传输机制的结构-性质关系的发展。这项研究计划提供了一个机会，使重要的进展，阐明关键的关系膜能够运输目标溶质的基础上化学，而不是空间，因素，应用远远超出稀土分离。此外，拟议的工作提供了一个新的，高通量的范例，以表征膜使用动态实验和MBDOE。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。

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 Identifying methods to separate REEs from domestic ore deposits or recycle them from outdated electronics is critical to maintaining a reliable supply of these materials.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.