CDS&E: Development of Computational Library for Accurate Binding Energies of Emerging Organic Contaminants on Environmental Interfaces

当化学品泄漏事件发生时，应急响应人员采取行动保护人类健康和环境。然而，关于溢出的化学品将如何相互作用并在环境中移动，它将最终到达哪里，或者它如何被过滤掉的信息并不总是可用的。在泄漏之后，能够快速预测这些行为的方法将是有用的。在化学系环境化学科学（ECS）项目的支持下，孟菲斯大学的威廉·亚历山大教授正在使用计算机模型快速估计污染物在泄漏后可能如何作用。亚历山大教授与他的学生一起开发了自动计算化学工具来预测污染物的物理性质，并预测“它们可能粘在哪里？“在环境或基础设施表面上。该小组正在建立一个与环境相关的表面模型数据库，并使用这些模型来计算筛选污染物可能粘附的表面。该小组的最终模型和工具可以让响应者快速估计新污染物的行为。这将有助于援助和指导补救战略，可能对公共安全和危机后经济复苏产生重大影响。该项目还为代表性不足的学生群体中的研究生和本科生提供多学科协作科学培训。外展工作旨在扩大家庭教育学生获得实验室科学的机会，重点是环境科学和化学实验室经验。从2004年到2014年，美国国家响应中心报告了约172，000起影响美国水体的化学品泄漏事件。其中一些是由于已知特性和毒性的化学品造成的，而对于其他化学品，可获得的信息相对较少。定性模型通常用于预测污染物的归宿和迁移。这些模型一般不处理重要的污染物行为的动力学方面，如溶剂化效应和构象平均。此外，环境中与污染物相互作用的大多数相关表面（淤泥、粘土等）和水系统内（过滤介质、聚合物管道等）都是动态的和无定形的将这些动力学方面纳入预测模型将提高所得物理性质和约束估计的准确性。在该项目中，输入文件和作业管理是自动化的快速量子力学（QM）预测污染物的物理性质，包括构象和溶剂化效应。产生了来自分子力学（MM）模拟的相关无定形表面模型（即无定形二氧化硅，碳，聚合物）的库。这些方法结合到QM/MM计划，使快速计算污染物的结合能直接过滤介质的选择和预测污染物可能集中。这些方法使用一系列化合物类别中的已知有机污染物分子的大型测试集进行验证，包括从公共卫生机构优先列表中选择的数十种化合物。选定的分子被用来实验验证计算的分子性质的新的分区和吸附研究。在整个调查过程中，开发了新的工作流程来管理大型数据集。除了准确预测污染物的行为，所产生的方法可能有助于减少人类暴露于污染物化合物在field.This奖项，需要更少的现场样品和时间，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。

When a chemical spill incident happens, emergency responders act to protect human health and the environment. However, information is not always available about how the spilled chemical will interact and move through the environment, where it will end up, or how it might be filtered out. After a spill, methods that can quickly predict these behaviors would be useful. With support from the Environmental Chemical Sciences (ECS) Program of the Chemistry Division, Professor William Alexander of The University of Memphis is using computer models to quickly estimate how contaminants may act after a spill. Working with his students, Professor Alexander develops automated computational chemistry tools to predict the physical properties of contaminants, and to predict "where they might stick?" on environmental or infrastructure surfaces. The group is building a data library of environmentally-relevant surface models and using these models to computationally screen what surfaces the contaminants may stick to. The group's resulting models and tools could allow responders to obtain rapid estimates of the behavior of novel contaminant compounds. This will help aid and direct remediation strategies that could have a significant impact on public safety and post crisis economic recovery. The project is also providing multidisciplinary collaborative scientific training for graduate and undergraduate students from underrepresented student populations. Outreach efforts are designed to expand access to laboratory science for home-schooled students, with a focus on environmental science and chemistry laboratory experiences.About 172,00 chemical spills impacting US bodies of water were reported to the US National Response Center from 2004-2014. Some of these are due to chemicals with known properties and toxicity, whereas for others, relatively little information is available. Qualitative models are often used to predict contaminant fate and transport. These models generally do not treat important dynamical aspects of contaminant behavior such as solvation effects and conformational averaging. Also, most relevant surfaces that contaminants interact with in the environment (silts, clays, etc.) and within the water system (filter media, polymer plumbing, etc.) are dynamical and amorphous in nature. Incorporating these dynamical aspects into prediction models will increase the accuracy of the resulting physical properties and binding estimates. In the project, input files and job management are automated for rapid quantum mechanical (QM) prediction of contaminant physical properties, including conformation and solvation effects. A library of relevant amorphous surface models (i.e. amorphous silica, carbon, polymers) derived from molecular mechanics (MM) simulations is generated. These approaches are combined into QM/MM schemes to enable rapid computation of contaminant binding energies to direct filter media selection and to predict where contaminants may concentrate. The methods are validated using a large test set of known organic contaminant molecules in a range of compound classes, including dozens of compounds selected from public health agency priority lists. Selected molecules are used to experimentally validate computed molecular properties by new partitioning and adsorption studies. Throughout the investigations, new workflows are developed to manage the large datasets. In addition to accurately predicting contaminant behavior, the resulting methods may help to reduce human exposure to contaminant compounds in the field by requiring less field samples and time.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.