Designing Deep Eutectic Solvents for Sustainable Separations

在过去的二十年里，新型的设计液体出现了，它们的性质可以通过改变分子水平的相互作用来调节。其中第一种是离子液体（ILs），它的排放量可以忽略不计，并且/或者比传统方法的能量需求要低得多，作为环保溶剂已经受到了相当大的关注。然而，集成电路的生产成本很高，阻碍了大规模应用。最近，被称为深共晶溶剂（DESs）的IL类似物出现了，它们表现出与IL相似的物理和化学性质，但生产成本要低得多，因为它们是由天然和可再生的无毒生物资源构成的。DESs作为环境友好型溶剂替代品具有巨大的潜力，可以为特定的分离定制设计，并已在催化，聚合物合成，气体分离和生物质处理等重要领域得到应用。设计这些新系统的一个关键挑战在于可以创建几乎无限数量的DESs。提出的研究重点是开发一种预测方法，为特定的分离过程设计最佳的DESs，然后可以利用它来改造化学分离行业。该提案的成功可能导致各种应用的环境友好型溶剂的高通量筛选。深共晶溶剂是Lewis或Bronsted酸和碱的液体，由固体氢键供体（HBD，如甘油）和固体氢键受体（HBA，通常是熔点高于IL的盐）组成，通过分子缔合形成共晶混合物。虽然IL混合物的摩尔比由其电荷决定，但DESs可以在HBD和HBA的不同摩尔比下形成，从而有效地实现无限组合。因此，DESs为设计更可持续的分离溶剂提供了巨大的机会；然而，合成如此大量的DESs的时间和成本给科学带来了挑战。因此，基于HBD、HBA和溶质之间的结构和相互作用的预测设计方法对于优化特定应用的DESs至关重要。该提案旨在通过建立基于群体贡献统计关联流体理论-可变范围（GC-SAFT-VR）的分子模型来解决DES设计问题。提出的研究重点是确定适合给定共晶溶剂的理论水平，确定最佳参数化过程，从而获得可预测的、可转移的参数，并验证模型。为了实现这些目标，将使用从头计算和分子模拟的信息来阐明DESs中潜在的分子相互作用；推导并应用液相理论的新发展来描述DESs中色散、排除体积、静电相互作用、电荷离域、氢键和极性的综合效应；并将这些新发展应用于基于统计关联流体理论的群体贡献框架内的实验DES系统。为拟议的研究开发的Python库将作为开源软件分发。一名研究生将接受高级分子状态方程的训练，几名本科生将参与该项目。还将为培训活动和向当地学校推广编制新的材料。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

In the last two decades, new classes of designer liquids have emerged whose properties can be tuned through changes to the molecular level interactions. The first of these, ionic liquids (ILs), which have negligible emissions and/or significantly lower energy requirements than traditional methods, have received considerable attention for use as environmentally friendly solvents. However, production of ILs can be costly, hindering large-scale application. More recently, IL analogues, termed deep eutectic solvents (DESs) have emerged, that show similar physical and chemical properties to ILs but are much cheaper to produce, as they are constituted from natural and renewable non-toxic bioresources. DESs have great potential as environmentally friendly solvent alternatives that can be custom designed for specific separations and have already found application in important areas such as catalysis, polymer synthesis, gas separation and biomass treatment. A key challenge for designing these new systems lies in the fact that a near infinite number of DESs can be created. The proposed research focuses on developing a predictive methodology for designing optimal DESs for specific separations processes, that can be then leveraged to transform the chemical separations industry. Success of the proposal is likely to lead to a high throughput screening of environmentally friendly solvents for various applications.Deep eutectic solvents are liquids of Lewis or Bronsted acids and bases that consist of a solid hydrogen bond donor (HBD; e.g., glycerol) and a solid hydrogen bond acceptor (HBA; frequently a salt with a higher melting point than an IL) that form a eutectic mixture through molecular association. While IL mixtures have fixed molar ratios dictated by their charges, DESs can be formed at different molar ratios of HBD and HBA, allowing effectively infinite combinations. DESs therefore offer a tremendous opportunity toward designing more sustainable solvents for separations; however, the time and cost of synthesis of such an impossibly large number of DESs creates a scientific challenge. As such, a predictive design methodology based upon the structure and interactions between the HBD, HBA and solutes, is crucial to optimize DESs for specific applications. The proposal seeks to address the question of DES design by developing molecular models based on group contribution statistical association fluid theory-variable range (GC-SAFT-VR). The proposed research focuses on identifying the level of theory suitable for a given eutectic solvent, determining the optimal parameterization process that leads to predictive, transferable parameters, and validating the models. To achieve these goals information from ab initio calculations and molecular simulations will be used to elucidate the underlying molecular interactions in DESs; derive and apply new developments in liquid state theory to describe the combined effects of dispersion, excluded volume, electrostatic interactions, charge delocalization, hydrogen bonding and polarity in DESs; and apply these new developments to experimental DES systems within a group-contribution framework based on the statistical associating fluid theory. Python libraries developed for the proposed research will be distributed as open-source software. A graduate student will be trained in advanced molecular-based equations of state and several undergraduate students will work on the project. New materials will also be developed for training activities and outreach to local area schools.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.