Award Abstract # 1808242
D3SC: Data-Driven Modeling and Experimental Investigation for Discovery of Aquatic Chemistry Reaction Kinetics: New Tools for Water Reuse Applications

NSF Org: CHE
Division Of Chemistry
Recipient: REGENTS OF THE UNIVERSITY OF CALIFORNIA AT RIVERSIDE
Initial Amendment Date: July 2, 2018
Latest Amendment Date: May 6, 2021
Award Number: 1808242
Award Instrument: Standard Grant
Program Manager: Anne-Marie Schmoltner
CHE
 Division Of Chemistry
MPS
 Directorate for Mathematical and Physical Sciences
Start Date: January 1, 2019
End Date: December 31, 2022 (Estimated)
Total Intended Award Amount: $439,301.00
Total Awarded Amount to Date: $501,059.00
Funds Obligated to Date: FY 2018 = $439,301.00
FY 2021 = $61,758.00
History of Investigator:
  • Bryan Wong (Principal Investigator)
    bryan.wong@ucr.edu
  • Haizhou Liu (Co-Principal Investigator)
Recipient Sponsored Research Office: University of California-Riverside
200 UNIVERSTY OFC BUILDING
RIVERSIDE
CA  US  92521-0001
(951)827-5535
Sponsor Congressional District: 39
Primary Place of Performance: University of California-Riverside
CA  US  92521-0001
Primary Place of Performance
Congressional District:
39
Unique Entity Identifier (UEI): MR5QC5FCAVH5
Parent UEI:
NSF Program(s): OFFICE OF MULTIDISCIPLINARY AC,
Environmental Chemical Science
Primary Program Source: 01001819DB NSF RESEARCH & RELATED ACTIVIT
01002122DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 062Z, 102Z, 1515, 9263
Program Element Code(s): 125300, 688200
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.049

ABSTRACT

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.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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Biswas, Sohag and Yamijala, Sharma S. R. K. C. and Wong, Bryan M. "Degradation of Per- and Polyfluoroalkyl Substances with Hydrated Electrons: A New Mechanism from First-Principles Calculations" Environmental Science & Technology , v.56 , 2022 https://doi.org/10.1021/acs.est.2c01469 Citation Details
Makam, Pandeeswar and Yamijala, Sharma S. R. K. C. and Bhadram, Venkata S. and Shimon, Linda J. W. and Wong, Bryan M. and Gazit, Ehud "Single amino acid bionanozyme for environmental remediation" Nature Communications , v.13 , 2022 https://doi.org/10.1038/s41467-022-28942-0 Citation Details
Patton, Samuel D. and Dodd, Michael C. and Liu, Haizhou "Degradation of 1,4-dioxane by reactive species generated during breakpoint chlorination: Proposed mechanisms and implications for water treatment and reuse" Journal of Hazardous Materials Letters , v.3 , 2022 https://doi.org/10.1016/j.hazl.2022.100054 Citation Details
Raza, Akber and Bardhan, Sharmistha and Xu, Lihua and Yamijala, Sharma S. and Lian, Chao and Kwon, Hyuna and Wong, Bryan M. "A Machine Learning Approach for Predicting Defluorination of Per- and Polyfluoroalkyl Substances (PFAS) for Their Efficient Treatment and Removal" Environmental Science & Technology Letters , v.6 , 2019 10.1021/acs.estlett.9b00476 Citation Details
Wang, Xian and Kumar, Anshuman and Shelton, Christian R. and Wong, Bryan M. "Harnessing deep neural networks to solve inverse problems in quantum dynamics: machine-learned predictions of time-dependent optimal control fields" Physical Chemistry Chemical Physics , v.22 , 2020 https://doi.org/10.1039/D0CP03694C Citation Details
Wu, Liang and Patton, Samuel D. and Liu, Haizhou "Mechanisms of oxidative removal of 1,4-dioxane via free chlorine rapidly mixing into monochloramine: Implications on water treatment and reuse" Journal of Hazardous Materials , v.440 , 2022 https://doi.org/10.1016/j.jhazmat.2022.129760 Citation Details
Yamijala, Sharma S. and Shinde, Ravindra and Wong, Bryan M. "Real-time degradation dynamics of hydrated per- and polyfluoroalkyl substances (PFASs) in the presence of excess electrons" Physical Chemistry Chemical Physics , v.22 , 2020 10.1039/C9CP06797C Citation Details
Yamijala, Sharma S.R.K.C. and Shinde, Ravindra and Hanasaki, Kota and Ali, Zulfikhar A. and Wong, Bryan M. "Photo-induced degradation of PFASs: Excited-state mechanisms from real-time time-dependent density functional theory" Journal of Hazardous Materials , v.423 , 2022 https://doi.org/10.1016/j.jhazmat.2021.127026 Citation Details

PROJECT OUTCOMES REPORT

Disclaimer

This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.

This NSF project combined computational techniques with laboratory measurements to understand chemical reactions that occur in wastewater during treatment and reuse. This project connected both theory and experiment in two different research groups to address the efficiency and reaction pathways of radical-organics interactions associated with water purification processes. In addition, since this project was funded under the NSF D3SC initiative, advanced machine-learning techniques were utilized in a data-driven approach to shed insight into the massive chemical contaminants that affect water quality. Over 8 peer-reviewed publications were generated from this project, many of which were featured on the cover of their respective journals. In particular, several of these journal publications demonstrated new computational techniques that were used for the first time in perfluoroalkyl and polyfluoroalkyl substances (PFAS) contaminants. For example, advanced quantum dynamical calculations used in this project (1) enabled a new capability for probing photo-induced mechanisms that could not be gleaned from conventional calculations previously carried out on PFAS and (2) provided a rationale for understanding ongoing experiments that are actively exploring photo-induced degradation of PFAS and other environmental contaminants. In regard to outreach and broader impacts, a high school student involved in the PI's group carried out research to broaden her computational skills. Her project on machine learning was selected by her high school to advance to the 2021 International Science Fair, which is the most prestigious accolade for science research at the high school level. Finally, the results of this project were selected as an Editor's Choice Award at the 2022 HPCwire Supercomputing Conference, a venue that covers the development of the fastest computers in the world and the scientists that utlize these resources (see image).


Last Modified: 05/03/2023
Modified by: Bryan M Wong

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