Designing Deep Eutectic Solvents for Sustainable Separations

在过去的二十年中，出现了新的设计师液体类别，其性质可以通过改变分子水平的相互作用来调整。其中第一种是离子液体（IL），其具有可忽略的排放和/或比传统方法显著更低的能量需求，作为环境友好的溶剂已经受到相当大的关注。然而，离子液体的生产可能是昂贵的，阻碍了大规模应用。最近，出现了称为深共熔溶剂（DES）的IL类似物，其显示出与IL相似的物理和化学性质，但生产成本低得多，因为它们由天然和可再生的无毒生物资源构成。DES作为环境友好的溶剂替代品具有很大的潜力，可以为特定的分离定制设计，并且已经在催化，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DES。拟议的研究重点是开发一种预测方法，用于为特定的分离过程设计最佳的DES，然后可以利用这种方法来改造化学分离行业。低共熔溶剂是刘易斯或布朗斯台德酸和碱的液体，其由固体氢键供体（HBD;例如，甘油）和固体氢键受体（HBA;通常是熔点高于IL的盐），它们通过分子缔合形成低共熔混合物。虽然IL混合物具有由其电荷决定的固定摩尔比，但DES可以在HBD和HBA的不同摩尔比下形成，从而允许有效地无限组合。因此，DES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DES的时间和成本带来了科学挑战。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针对特定应用优化DES至关重要。 该提案旨在解决DES设计的问题，通过开发分子模型的基础上基团贡献统计缔合流体理论变量范围（GC-SAFT-VR）。拟议的研究重点是确定适用于给定的低共熔溶剂的理论水平，确定最佳的参数化过程，导致预测，可转移的参数，并验证模型。为了实现这些目标，从头计算和分子模拟的信息将用于阐明DES中潜在的分子相互作用;推导并应用液态理论的新发展来描述DES中分散、排除体积、静电相互作用、电荷离域、氢键和极性的综合效应;并将这些新的发展应用于基于统计缔合流体理论的基团贡献框架内的实验DES系统。为拟议研究开发的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培训，几名本科生将参与该项目。该奖项反映了NSF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.