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Farahbakhsh J, Golgoli M, Khiadani M, Najafi M, Suwaileh W, Razmjou A, Zargar M. Recent advances in surface tailoring of thin film forward osmosis membranes: A review. CHEMOSPHERE 2024; 346:140493. [PMID: 37890801 DOI: 10.1016/j.chemosphere.2023.140493] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/05/2023] [Revised: 10/03/2023] [Accepted: 10/18/2023] [Indexed: 10/29/2023]
Abstract
The recent advancements in fabricating forward osmosis (FO) membranes have shown promising results in desalination and water treatment. Different methods have been applied to improve FO performance, such as using mixed or new draw solutions, enhancing the recovery of draw solutions, membrane modification, and developing FO-hybrid systems. However, reliable methods to address the current issues, including reverse salt flux, fouling, and antibacterial activities, are still in progress. In recent decades, surface modification has been applied to different membrane processes, including FO membranes. Introducing nanochannels, bioparticles, new monomers, and hydrophilic-based materials to the surface layer of FO membranes has significantly impacted their performance and efficiency and resulted in better control over fouling and concentration polarization (CP) in these membranes. This review critically investigates the recent developments in FO membrane processes and fabrication techniques for FO surface-layer modification. In addition, this study focuses on the latest materials and structures used for the surface modification of FO membranes. Finally, the current challenges, gaps, and suggestions for future studies in this field have been discussed in detail.
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Affiliation(s)
- Javad Farahbakhsh
- School of Engineering, Edith Cowan University, Joondalup, WA, 6027, Australia
| | - Mitra Golgoli
- School of Engineering, Edith Cowan University, Joondalup, WA, 6027, Australia
| | - Mehdi Khiadani
- School of Engineering, Edith Cowan University, Joondalup, WA, 6027, Australia
| | - Mohadeseh Najafi
- School of Engineering, Edith Cowan University, Joondalup, WA, 6027, Australia
| | - Wafa Suwaileh
- Chemical Engineering Program, Texas A&M University at Qatar, Education City, Doha, Qatar
| | - Amir Razmjou
- School of Engineering, Edith Cowan University, Joondalup, WA, 6027, Australia; School of Civil and Environmental Engineering, University of Technology Sydney (UTS), City Campus, Broadway, NSW, 2007, Australia; Mineral Recovery Research Center (MRRC), School of Engineering, Edith Cowan University, Joondalup, Perth, WA, 6027, Australia
| | - Masoumeh Zargar
- School of Engineering, Edith Cowan University, Joondalup, WA, 6027, Australia; Mineral Recovery Research Center (MRRC), School of Engineering, Edith Cowan University, Joondalup, Perth, WA, 6027, Australia.
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Pathan S, Islam SS, Sen Gupta R, Maity B, Reddy PR, Mandal S, Anki Reddy K, Bose S. Fundamental Understanding of Ultrathin, Highly Stable Self-Assembled Liquid Crystalline Graphene Oxide Membranes Leading to Precise Molecular Sieving through Non-equilibrium Molecular Dynamics. ACS NANO 2023; 17:7272-7284. [PMID: 37036338 DOI: 10.1021/acsnano.2c10300] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Self-assembled graphene oxide lyotropic liquid crystal (GO LLC) structures are mostly formed in aqueous medium; however, most GO derivatives are water insoluble, so processing GO LLCs in water poses a practical limitation. The use of polar aprotic solvent (like dimethyl sulfoxide) for the formation of GO LLC structures would be interesting, because it would allow incorporating additives, like photoinitiators or cross-linkers, or blending with polymers that are insoluble in water, which hence would expand its scope. The well-balanced electrostatic interaction between DMSO and GO can promote and stabilize the GO nanosheets' alignment even at lower concentrations. With this in mind, herein we report mechanically robust, chlorine-tolerant, self-assembled nanostructured GO membranes for precise molecular sieving. Small-angle X-ray scattering and polarized optical microscopy confirmed the alignment of the modified GO nanosheets in polar aprotic solvent, and the LLC structure was effectively preserved even after cross-linking under UV light. We found that the modified GO membranes exhibited considerably improved salt rejection for monovalent ions (99%) and water flux (120 LMH) as compared to the shear-aligned GO membrane, which is well supported by forward osmosis simulation studies. Additionally, our simulation studies indicated that water molecules traveled a longer path while permeating through the GO membrane compared to the GO LLC membrane. Consequently, salt ions permeate slowly across the GO LLC membrane, yielding higher salt rejection than the GO membrane. This begins to suggest strong electrostatic repulsion with the salt ions, causing higher salt rejection in the GO LLC membrane. We foresee that the ordered cross-linked GO sheets contributed to excellent mechanical stability under a high-pressure, cross-flow, chlorine environment. Overall, these membranes are easily scalable, exhibit good mechanical stability, and represent a breakthrough for the potential use of polymerized GO LLC membranes in practical water remediation applications.
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Affiliation(s)
- Shabnam Pathan
- Department of Materials Engineering, Indian Institute of Science, Bengaluru-560012, India
| | - Sk Safikul Islam
- Department of Materials Engineering, Indian Institute of Science, Bengaluru-560012, India
| | - Ria Sen Gupta
- Department of Materials Engineering, Indian Institute of Science, Bengaluru-560012, India
| | - Barnali Maity
- Department of Materials Engineering, Indian Institute of Science, Bengaluru-560012, India
| | - P Rajasekhar Reddy
- Department of Chemical Engineering, Indian Institute of Technology, Guwahati-781039, Assam, India
| | - Samir Mandal
- Department of Materials Engineering, Indian Institute of Science, Bengaluru-560012, India
| | - K Anki Reddy
- Department of Chemical Engineering, Indian Institute of Technology Tirupati, Tirupati-517619, Andhra Pradesh, India
| | - Suryasarathi Bose
- Department of Materials Engineering, Indian Institute of Science, Bengaluru-560012, India
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Park SJ, Lee S, Lee D, Lee NE, Park JS, Hong JH, Jang JW, Kim H, Roh S, Lee G, Lee D, Cho SY, Park C, Lee DG, Lee R, Nho D, Yoon DS, Yoo YK, Lee JH. PCR-like performance of rapid test with permselective tunable nanotrap. Nat Commun 2023; 14:1520. [PMID: 36934093 PMCID: PMC10024276 DOI: 10.1038/s41467-023-37018-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Accepted: 02/24/2023] [Indexed: 03/20/2023] Open
Abstract
Highly sensitive rapid testing for COVID-19 is essential for minimizing virus transmission, especially before the onset of symptoms and in asymptomatic cases. Here, we report bioengineered enrichment tools for lateral flow assays (LFAs) with enhanced sensitivity and specificity (BEETLES2), achieving enrichment of SARS-CoV-2 viruses, nucleocapsid (N) proteins and immunoglobulin G (IgG) with 3-minute operation. The limit of detection is improved up to 20-fold. We apply this method to clinical samples, including 83% with either intermediate (35%) or low viral loads (48%), collected from 62 individuals (n = 42 for positive and n = 20 for healthy controls). We observe diagnostic sensitivity, specificity, and accuracy of 88.1%, 100%, and 91.9%, respectively, compared with commercial LFAs alone achieving 14.29%, 100%, and 41.94%, respectively. BEETLES2, with permselectivity and tunability, can enrich the SARS-CoV-2 virus, N proteins, and IgG in the nasopharyngeal/oropharyngeal swab, saliva, and blood serum, enabling reliable and sensitive point-of-care testing, facilitating fast early diagnosis.
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Affiliation(s)
- Seong Jun Park
- Department of Electrical Engineering, Kwangwoon University, 20 Kwangwoon-ro, Nowon, Seoul, 01897, Republic of Korea
| | - Seungmin Lee
- Department of Electrical Engineering, Kwangwoon University, 20 Kwangwoon-ro, Nowon, Seoul, 01897, Republic of Korea
- School of Biomedical Engineering, Korea University, 145 Anam-ro, Seongbuk, Seoul, 02841, Republic of Korea
| | - Dongtak Lee
- School of Biomedical Engineering, Korea University, 145 Anam-ro, Seongbuk, Seoul, 02841, Republic of Korea
| | - Na Eun Lee
- Department of Electrical Engineering, Kwangwoon University, 20 Kwangwoon-ro, Nowon, Seoul, 01897, Republic of Korea
- Department of Biotechnology, College of Life Sciences and Biotechnology, Korea University, Seoul, 02841, Republic of Korea
| | - Jeong Soo Park
- Department of Electrical Engineering, Kwangwoon University, 20 Kwangwoon-ro, Nowon, Seoul, 01897, Republic of Korea
| | - Ji Hye Hong
- Department of Electrical Engineering, Kwangwoon University, 20 Kwangwoon-ro, Nowon, Seoul, 01897, Republic of Korea
- School of Biomedical Engineering, Korea University, 145 Anam-ro, Seongbuk, Seoul, 02841, Republic of Korea
| | - Jae Won Jang
- School of Biomedical Engineering, Korea University, 145 Anam-ro, Seongbuk, Seoul, 02841, Republic of Korea
- Interdisciplinary Program in Precision Public Health, Korea University, Seoul, 02841, Republic of Korea
| | - Hyunji Kim
- School of Biomedical Engineering, Korea University, 145 Anam-ro, Seongbuk, Seoul, 02841, Republic of Korea
- Interdisciplinary Program in Precision Public Health, Korea University, Seoul, 02841, Republic of Korea
| | - Seokbeom Roh
- Department of Biotechnology and Bioinformatics, Korea University, Sejong, 30019, Republic of Korea
- Interdisciplinary Graduate Program for Artificial Intelligence Smart Convergence Technology, Korea University, Sejong, 30019, Korea
| | - Gyudo Lee
- Department of Biotechnology and Bioinformatics, Korea University, Sejong, 30019, Republic of Korea
- Interdisciplinary Graduate Program for Artificial Intelligence Smart Convergence Technology, Korea University, Sejong, 30019, Korea
| | - Dongho Lee
- CALTH Inc., Changeop-ro 54, Seongnam, Gyeonggi, 13449, Republic of Korea
| | - Sung-Yeon Cho
- Vaccine Bio Research Institute, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Division of Infectious Diseases, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Chulmin Park
- Vaccine Bio Research Institute, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Dong-Gun Lee
- Vaccine Bio Research Institute, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Division of Infectious Diseases, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Raeseok Lee
- Vaccine Bio Research Institute, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Division of Infectious Diseases, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Dukhee Nho
- Vaccine Bio Research Institute, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Division of Infectious Diseases, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Dae Sung Yoon
- School of Biomedical Engineering, Korea University, 145 Anam-ro, Seongbuk, Seoul, 02841, Republic of Korea.
- Interdisciplinary Program in Precision Public Health, Korea University, Seoul, 02841, Republic of Korea.
- Astrion Inc, Seoul, 02841, Republic of Korea.
| | - Yong Kyoung Yoo
- Department of Electronic Engineering, Catholic Kwandong University, 24, Beomil-ro 579 beon-gil, Gangneung-si, Gangwon-do, 25601, Republic of Korea.
| | - Jeong Hoon Lee
- Department of Electrical Engineering, Kwangwoon University, 20 Kwangwoon-ro, Nowon, Seoul, 01897, Republic of Korea.
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Luo J, Li M, Hoek EMV, Heng Y. Supercomputing and machine learning-aided optimal design of high permeability seawater reverse osmosis membrane systems. Sci Bull (Beijing) 2023:S2095-9273(23)00075-0. [PMID: 36774298 DOI: 10.1016/j.scib.2023.01.039] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2022] [Revised: 01/06/2023] [Accepted: 01/28/2023] [Indexed: 02/05/2023]
Abstract
Concentration polarization (CP) should limit the energy and cost reducing benefits of high permeability seawater reverse osmosis (SWRO) membranes operating at a water flux higher than normal one. Herein, we propose a multiscale optimization framework coupling membrane permeability, feed spacer design (sub-millimeter scale) and system design (meter scale) via computational fluid dynamics and system level modeling using advanced supercomputing in conjunction with machine learning. Simulation results suggest energy consumption could be reduced by 27.5% (to 1.66 kWh m-3) predominantly through the use of high permeability SWRO membranes (12.2%) and a two-stage design (14.5%). Without optimization, CP approaches 1.52 at the system inlet, whereas the optimized CP is limited to 1.20. This work elucidates the optimized permeability, module design, operating scheme and benefits of high permeability SWRO membranes in seawater desalination.
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Affiliation(s)
- Jiu Luo
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou 510006, China; National Supercomputing Center in Guangzhou (NSCC-GZ), Guangzhou 510006, China; Guangdong Province Key Laboratory of Computational Science, Guangzhou 510006, China
| | - Mingheng Li
- Department of Chemical and Materials Engineering, California State Polytechnic University, Pomona CA 91768, USA
| | - Eric M V Hoek
- Department of Civil & Environmental Engineering, California NanoSystems Institute and Institute of the Environment & Sustainability, University of California, Los Angeles (UCLA), Los Angeles CA 90095, USA; Energy Storage & Distributed Resources Division, Lawrence Berkeley National Laboratory, Berkeley CA 94720, USA
| | - Yi Heng
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou 510006, China; National Supercomputing Center in Guangzhou (NSCC-GZ), Guangzhou 510006, China; Guangdong Province Key Laboratory of Computational Science, Guangzhou 510006, China.
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