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D3SC: Data-Driven Modeling and Experimental Investigation for Discovery of Aquatic Chemistry Reaction Kinetics: New Tools for Water Reuse Applications

D3SC：用于发现水生化学反应动力学的数据驱动建模和实验研究：水回用应用的新工具

基本信息

批准号：
1808242
负责人：
Bryan Wong
金额：
$ 43.93万
依托单位：
University of California-Riverside
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-01-01 至 2022-12-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1808242&HistoricalAwards=false
关键词：
D3SC Data Driven Modeling Experimental

项目摘要

This award is supported by the Environmental Chemical Sciences Program in the NSF Chemistry Division. Professors Bryan M. Wong and Haizhou Liu at the University of California-Riverside combine computational techniques with laboratory measurements to understand changes in chemical reactions that occur in wastewater during treatment and reuse. The researchers seek to understand the rate of oxidation reactions of organic compounds in water by reactive species called radicals. The computational tools used include data visualization, data mining, machine learning, and data analytics techniques. Predictive theoretical models and quantum-based methods are developed that are applicable to water reuse applications. With these, the chemical reaction rates for water reuse are calculated. The models are then validated and improved through targeted experiments. This approach advances the basic scientific understanding of reaction dynamics in molecular radicals. This project allows a seamless connection of both theory and experiment to address the efficiency and reaction pathways of radical-organics interactions associated with water purification processes. Professors Wong and Liu engage students at all levels in their research, including community college students. A total of three Hispanic-Serving Institutions are involved in this project. The investigators also reach out to K-12 students and their teachers to promote understanding of the role of computing in environmental science and engineering. By examining wastewater treatments, this project promotes human health and sustainability efforts in industry. This project addresses aquatic reaction kinetics for advanced water treatment and reuse. The combined multidisciplinary approach leads to a systematic understanding of how molecular structure influences thermodynamics and kinetics in complex aqueous environments. This is a significant scientific and technical challenge and critical to providing a guided, rational path for improving water reuse. Furthermore, this project establishes a computational screening effort to identify fundamental physicochemical characteristics that affect aqueous chemical kinetics. Both density functional theory (DFT) and rigorous many-body wave function CCSD(T)-F12 methods are used. The project also establishes a series of guided kinetics and characterization efforts that closely follow the computational screening efforts, including numerical sensitivity analyses. These calculations provide a systematic understanding of electronic structure of organic molecules in aqueous environments. The experiments in turn guide the computational studies and afford the capability to understand the detailed, complex contributions that modulate the reaction dynamics in these aqueous systems. The fundamental knowledge generated by this work has broad societal impact, particularly for regions and industries that critically rely on water treatment and reuse.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.

该奖项由NSF化学部的环境化学科学计划支持。 Bryan M.加州大学河滨分校的Wong和Haizhou Liu将联合收割机计算技术与实验室测量相结合，以了解废水处理和再利用过程中发生的化学反应的变化。研究人员试图了解水中有机化合物通过称为自由基的活性物质氧化反应的速率。使用的计算工具包括数据可视化、数据挖掘、机器学习和数据分析技术。开发了适用于水再利用应用的预测理论模型和基于量子的方法。据此计算了中水回用的化学反应速率。然后通过有针对性的实验对模型进行验证和改进。这种方法推进了对分子自由基反应动力学的基本科学理解。该项目允许理论和实验的无缝连接，以解决与水净化过程相关的自由基-有机物相互作用的效率和反应途径。黄教授和刘教授在他们的研究中吸引了各个层次的学生，包括社区学院的学生。共有三个西班牙裔服务机构参与了这一项目。调查人员还接触到K-12学生和他们的老师，以促进对计算在环境科学和工程中的作用的理解。通过研究废水处理，该项目促进了人类健康和工业可持续发展的努力。本计画探讨水反应动力学在水处理与再利用之应用。结合多学科的方法导致分子结构如何影响复杂的水环境中的热力学和动力学的系统理解。这是一项重大的科学和技术挑战，对于为改进水的再利用提供有指导的合理途径至关重要。此外，该项目建立了一个计算筛选的努力，以确定影响水化学动力学的基本物理化学特性。采用密度泛函理论（DFT）和严格的多体波函数CCSD（T）-F12方法。该项目还建立了一系列指导动力学和表征工作，密切关注计算筛选工作，包括数值敏感性分析。这些计算提供了一个系统的理解，在水环境中的有机分子的电子结构。这些实验反过来又指导了计算研究，并提供了理解这些水溶液体系中调节反应动力学的详细、复杂贡献的能力。这项工作产生的基础知识具有广泛的社会影响，特别是对严重依赖水处理和再利用的地区和行业。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。

项目成果

期刊论文数量（8）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

A Machine Learning Approach for Predicting Defluorination of Per- and Polyfluoroalkyl Substances (PFAS) for Their Efficient Treatment and Removal

DOI：
10.1021/acs.estlett.9b00476
发表时间：
2019-10-01
期刊：
ENVIRONMENTAL SCIENCE & TECHNOLOGY LETTERS
影响因子：
10.9
作者：
Raza, Akber;Bardhan, Sharmistha;Wong, Bryan M.
通讯作者：
Wong, Bryan M.

Photo-induced degradation of PFASs: Excited-state mechanisms from real-time time-dependent density functional theory

光诱导的 PFAS 降解：来自实时时间依赖密度泛函理论的激发态机制

DOI：
10.1016/j.jhazmat.2021.127026
发表时间：
2022
期刊：
Journal of Hazardous Materials
影响因子：
13.6
作者：
Yamijala, Sharma S.R.K.C.;Shinde, Ravindra;Hanasaki, Kota;Ali, Zulfikhar A.;Wong, Bryan M.
通讯作者：
Wong, Bryan M.

Degradation of 1,4-dioxane by reactive species generated during breakpoint chlorination: Proposed mechanisms and implications for water treatment and reuse

断点氯化过程中产生的活性物质降解 1,4-二恶烷：拟议的机制以及对水处理和再利用的影响