High-Throughput Computational Screening of Two-Dimensional Covalent Organic Frameworks (2D COFs) for Capturing Radon in Moist Air
Radon (Rn) and its decay products are the primary sources of natural ionizing radiation exposure for the public, posing significant health risks, including being a leading cause of lung cancer. Porous material-based adsorbents offer a feasible and efficient solution for controlling Rn concentrations...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-05-01
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| Series: | Nanomaterials |
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| Online Access: | https://www.mdpi.com/2079-4991/13/9/1532 |
| _version_ | 1797602066633326592 |
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| author | Hongyan Zeng Xiaomin Geng Shitong Zhang Bo Zhou Shengtang Liu Zaixing Yang |
| author_facet | Hongyan Zeng Xiaomin Geng Shitong Zhang Bo Zhou Shengtang Liu Zaixing Yang |
| author_sort | Hongyan Zeng |
| collection | DOAJ |
| description | Radon (Rn) and its decay products are the primary sources of natural ionizing radiation exposure for the public, posing significant health risks, including being a leading cause of lung cancer. Porous material-based adsorbents offer a feasible and efficient solution for controlling Rn concentrations in various scenes to achieve safe levels. However, due to competitive adsorption between Rn and water, finding candidates with a higher affinity and capacity for capturing Rn in humid air remains a significant challenge. Here, we conducted high-throughput computational screening of 8641 two-dimensional covalent organic frameworks (2D COFs) in moist air using grand canonical Monte Carlo simulations. We identified the top five candidates and revealed the structure–performance relationship. Our findings suggest that a well-defined cavity with an approximate spherical inner space, with a diameter matching that of Rn, is the structural basis for a proper Rn capturing site. This is because the excellent steric match between the cavity and Rn maximizes their van der Waals dispersion interactions. Additionally, the significant polarization electrostatic potential surface of the cavity can regulate the adsorption energy of water and ultimately impact Rn selectivity. Our study offers a potential route for Rn management using 2D COFs in moist air and provides a scientific basis for further experimentation. |
| first_indexed | 2024-03-11T04:11:42Z |
| format | Article |
| id | doaj.art-f906b2f3eb5b425eac86054593c10f60 |
| institution | Directory Open Access Journal |
| issn | 2079-4991 |
| language | English |
| last_indexed | 2024-03-11T04:11:42Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Nanomaterials |
| spelling | doaj.art-f906b2f3eb5b425eac86054593c10f602023-11-17T23:27:20ZengMDPI AGNanomaterials2079-49912023-05-01139153210.3390/nano13091532High-Throughput Computational Screening of Two-Dimensional Covalent Organic Frameworks (2D COFs) for Capturing Radon in Moist AirHongyan Zeng0Xiaomin Geng1Shitong Zhang2Bo Zhou3Shengtang Liu4Zaixing Yang5State Key Laboratory of Radiation Medicine and Protection, School for Radiological and Inter-Disciplinary Sciences (RAD-X), Collaborative Innovation Center of Radiation Medicine of Jiangsu Higher Education Institutions, Soochow University, Suzhou 215123, ChinaState Key Laboratory of Radiation Medicine and Protection, School for Radiological and Inter-Disciplinary Sciences (RAD-X), Collaborative Innovation Center of Radiation Medicine of Jiangsu Higher Education Institutions, Soochow University, Suzhou 215123, ChinaState Key Laboratory of Separation Membranes and Membrane Processes, School of Chemical Engineering and Technology, Tiangong University, Tianjin 300387, ChinaSchool of Big Data and Artificial Intelligence, Chengdu Technological University, Chengdu 611730, ChinaState Key Laboratory of Radiation Medicine and Protection, School for Radiological and Inter-Disciplinary Sciences (RAD-X), Collaborative Innovation Center of Radiation Medicine of Jiangsu Higher Education Institutions, Soochow University, Suzhou 215123, ChinaState Key Laboratory of Radiation Medicine and Protection, School for Radiological and Inter-Disciplinary Sciences (RAD-X), Collaborative Innovation Center of Radiation Medicine of Jiangsu Higher Education Institutions, Soochow University, Suzhou 215123, ChinaRadon (Rn) and its decay products are the primary sources of natural ionizing radiation exposure for the public, posing significant health risks, including being a leading cause of lung cancer. Porous material-based adsorbents offer a feasible and efficient solution for controlling Rn concentrations in various scenes to achieve safe levels. However, due to competitive adsorption between Rn and water, finding candidates with a higher affinity and capacity for capturing Rn in humid air remains a significant challenge. Here, we conducted high-throughput computational screening of 8641 two-dimensional covalent organic frameworks (2D COFs) in moist air using grand canonical Monte Carlo simulations. We identified the top five candidates and revealed the structure–performance relationship. Our findings suggest that a well-defined cavity with an approximate spherical inner space, with a diameter matching that of Rn, is the structural basis for a proper Rn capturing site. This is because the excellent steric match between the cavity and Rn maximizes their van der Waals dispersion interactions. Additionally, the significant polarization electrostatic potential surface of the cavity can regulate the adsorption energy of water and ultimately impact Rn selectivity. Our study offers a potential route for Rn management using 2D COFs in moist air and provides a scientific basis for further experimentation.https://www.mdpi.com/2079-4991/13/9/1532radon capturetwo-dimensional covalent organic frameworksvirtual screeningstructure–performance relationshipGCMC simulations |
| spellingShingle | Hongyan Zeng Xiaomin Geng Shitong Zhang Bo Zhou Shengtang Liu Zaixing Yang High-Throughput Computational Screening of Two-Dimensional Covalent Organic Frameworks (2D COFs) for Capturing Radon in Moist Air Nanomaterials radon capture two-dimensional covalent organic frameworks virtual screening structure–performance relationship GCMC simulations |
| title | High-Throughput Computational Screening of Two-Dimensional Covalent Organic Frameworks (2D COFs) for Capturing Radon in Moist Air |
| title_full | High-Throughput Computational Screening of Two-Dimensional Covalent Organic Frameworks (2D COFs) for Capturing Radon in Moist Air |
| title_fullStr | High-Throughput Computational Screening of Two-Dimensional Covalent Organic Frameworks (2D COFs) for Capturing Radon in Moist Air |
| title_full_unstemmed | High-Throughput Computational Screening of Two-Dimensional Covalent Organic Frameworks (2D COFs) for Capturing Radon in Moist Air |
| title_short | High-Throughput Computational Screening of Two-Dimensional Covalent Organic Frameworks (2D COFs) for Capturing Radon in Moist Air |
| title_sort | high throughput computational screening of two dimensional covalent organic frameworks 2d cofs for capturing radon in moist air |
| topic | radon capture two-dimensional covalent organic frameworks virtual screening structure–performance relationship GCMC simulations |
| url | https://www.mdpi.com/2079-4991/13/9/1532 |
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