NSF Award
2417921
NON-TECHNICAL SUMMARY<br/><br/>Access to clean water is a crucial global challenge impacting health, biodiversity, and geopolitics. Approximately half a billion people lack sufficient access to clean water, emphasizing the need for improved water treatment methods. Traditional techniques, such as using activated carbon and ion exchange resins, are ineffective against certain pollutants like siloxanes. This project aims to develop advanced materials called Metal-Organic-Frameworks (MOFs) that can efficiently remove these harmful siloxanes from water. Our approach involves using cutting-edge computer simulations and machine learning to identify the best MOFs for siloxane removal. The cumulative insights from this project and its extensions will pave the way for the large-scale removal of multiple pollutants from water, contributing significantly to the production of clean and portable water on a global scale.<br/><br/>This innovative research will not only enhance water purification but also provide valuable hands-on experience for students, particularly those from under-represented minority groups. By integrating advanced research into education, the project will foster the development of future scientists and engineers, promoting diversity and inclusion in STEM fields. Furthermore, this initiative will establish a robust computational chemistry group at Texas Woman’s University, creating opportunities for collaboration and enhancing the educational experience for students in chemistry, biochemistry, and environmental science. These efforts will create a sustainable impact by preparing future scientists and engineers and by contributing to the long-term goal of providing clean and portable water on a global scale. <br/><br/><br/>TECHNICAL SUMMARY<br/><br/>Clean water scarcity represents a global crisis with far-reaching implications for human health, biodiversity, and geopolitics. Approximately half a billion people worldwide lack access to clean water, underscoring the urgent need for efficient water treatment techniques. High-performance adsorbents offer a low-cost, energy-efficient, and environmentally friendly solution for removing target pollutants from water. However, commonly employed adsorbents such as activated carbon, zeolites, ion exchange resins, and wastewater treatment plants fall short of effectively addressing siloxanes, a problematic class of organic pollutants in water. Recognizing this gap, our project aims to assess Metal-Organic-Frameworks (MOFs) as promising adsorbents for siloxane removal. The research plan comprises three key tasks: screening over 100 representative MOFs against four linear and three cyclic siloxanes using Forcefield-based Grand Canonical Monte Carlo (GCMC) simulations; studying the adsorption mechanisms of the identified MOFs through Density Functional Theory (DFT) computations to understand molecular-level interactions between MOFs and siloxanes; and leveraging advanced machine learning (ML) classification algorithms to unveil structure-performance relationships within MOFs, enabling the prediction of MOFs with optimal adsorption capabilities for effectively removing multiple siloxanes in water treatment. This project establishes the groundwork for our overarching objective: the development of a reliable and automated method for discovering adsorbents capable of effectively removing diverse pollutants from water, thus addressing critical global water crisis challenges. The predictions generated for promising MOFs not only offer valuable guidance but also serve as a basis for experimental verification. The innovative GCMC-DFT-ML strategy, along with machine learning predictions, acts as a cornerstone for subsequent investigations. These include the selection of forcefields for MOF structures, examinations of solvent effects, and explorations of adsorption performance for various porous materials targeting a spectrum of water pollutants.<br/><br/>The cumulative insights from this project and its extensions will pave the way for the large-scale removal of multiple pollutants from water, contributing significantly to the production of clean and portable water on a global scale. Additionally, this project will significantly contribute to LEAPS (Launch of Early-Career Academic Pathways in STEM) goals by providing extensive research training for students, particularly those from underrepresented minority groups. This training will enhance their skills and career prospects in science and engineering, fostering diversity and inclusion in STEM fields. Establishing a robust computational chemistry group at Texas Woman’s University will promote interdisciplinary collaboration and integrate research with undergraduate education. These efforts will not only prepare future scientists and engineers but also contribute to the long-term goal of providing clean and portable water on a global scale.<br/><br/><br/>STATEMENT OF MERIT REVIEW<br/><br/>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.
