[1] Mehrifar, Y. F., Ghasemi Koozekonan, A. F., Rashidi, M. A. F., Arab, N. F., & Farhang Dehghan, S. F. (2024). A review on H2S adsorption by metal-organic frameworks for
air purification application. Advances in Environmental Technology, 10(3), 270-296. https://doi.org/10.22104/aet.2024.6818.1863
[2] Kishore Chand, A. A., Bajer, B., Schneider, E. S., Mantel, T., Ernst, M., Filiz, V., & Glass, S. (2022). Modification of polyacrylonitrile ultrafiltration membranes to enhance the adsorption of cations and anions. Membranes, 12(6), 580. https://doi.org/10.3390/membranes12060580
[3] Ibrahim, S., Mohammadi Ghaleni, M., Isloor, A. M., Bavarian, M., & Nejati, S. (2020). Poly (Homopiperazine–Amide) thin-film composite membrane for nanofiltration of heavy metal ions. ACS omega, 5(44), 28749-28759. https://doi.org/10.1021/acsomega.0c04064.
[4] Hu, Y. (2023). Grand challenge in membrane applications: Liquid. Frontiers in Membrane Science and Technology, 2, 1177528. https://doi.org/10.3389/frmst.2023.1177528
[5] Isloor, A. M., Nayak, M. C., Prabhu, B., Ismail, N., Ismail, A. F., & Asiri, A. M. (2019). Novel polyphenylsulfone (PPSU)/nano tin oxide (SnO2) mixed matrix ultrafiltration hollow fiber membranes: Fabrication, characterization and toxic dyes removal from aqueous solutions. Reactive and Functional Polymers, 139, 170-180. https://doi.org/10.1016/j.reactfunctpolym.2019.02.015
[6] Moch Jr, I. (2000). Membranes, hollow‐fiber. Kirk‐Othmer Encyclopedia of Chemical Technology. https://doi.org/10.1002/0471238961.0815121213150308.a01.pub2
[7] Jonkers, W. A., Cornelissen, E. R., & de Vos, W. M. (2023). Hollow fiber nanofiltration: From lab-scale research to full-scale applications. Journal of Membrane Science, 669, 121234. https://doi.org/10.1016/j.memsci.2023.121234.
[8] Mokarizadeh, H., Moayedfard, S., Maleh, M. S., Mohamed, S. I. G. P., Nejati, S., & Esfahani, M. R. (2021). The role of support layer properties on the fabrication and performance of thin-film composite membranes: The significance of selective layer-support layer connectivity. Separation and Purification Technology, 278, 119451. https://doi.org/10.1016/j.seppur.2021.119451
[9] Karki, S., Hazarika, G., Yadav, D., & Ingole, P. G. (2024). Polymeric membranes for industrial applications: Recent progress, challenges and perspectives. Desalination, 573, 117200. https://doi.org/10.1016/j.desal.2024.117200.
[10] Pasaoglu, M. E., Kaya, R., & Koyuncu, I. (2023). Novel Membrane Technologies in the Treatment and Recovery of Wastewaters. In Wastewater Management and Technologies (pp. 87-106). Cham: Springer Nature Switzerland. https://link.springer.com/chapter/10.1007/978-3-031-36298-9_7
[11] Vali, H., Sadeghi, A., Shafiee, M. J., Barzegar, M., & Rahimpour, M. R. (2024). Carbon capture with mixed-matrix membrane. In Handbook of Membrane Separations: Chemical, Gas, and Water Applications. Elsevier. https://doi.org/10.1016/b978-0-323-93940-9.00269-3
[12] Hamid, M. R. A., & Jeong, H. K. (2018). Recent advances on mixed-matrix membranes for gas separation: Opportunities and engineering challenges. Korean Journal of Chemical Engineering, 35(8), 1577-1600. https://doi.org/10.1007/s11814-018-0081-1
[13] Xu, X., Yang, Y., Liu, T., & Chu, B. (2022). Cost-effective polymer-based membranes for drinking water purification. Giant, 10, 100099. https://doi.org/10.1016/j.giant.2022.100099.
[14] Du, Y., Pramanik, B. K., Zhang, Y., & Jegatheesan, V. (2023). Resource recovery from RO concentrate using nanofiltration: Impact of active layer thickness on performance. Environmental Research, 231, 116265. https://doi.org/10.1016/j.envres.2023.116265
[15] Ibrahim, G. S., Isloor, A. M., Moslehyani, A., & Ismail, A. F. (2017). Bio-inspired, fouling resistant, tannic acid functionalized halloysite nanotube reinforced polysulfone loose nanofiltration hollow fiber membranes for efficient dye and salt separation. Journal of Water Process https://doi.org/10.1016/j.jwpe.2017.09.015
[16] Tuncay, G., Keskin, B., Türken, T., Vatanpour, V., & Koyuncu, I. (2022). Development of braid reinforced hollow fiber membranes as both ultrafiltration and nanofiltration membranes:
Effect of pore forming additive on structure and performance. Journal of Applied Polymer Science, 139(44). https://doi.org/10.1002/app.53098
[17] Syed Ibrahim, G. P., Isloor, A. M., Ismail, A. F., & Farnood, R. (2020). One-step synthesis of zwitterionic graphene oxide nanohybrid: Application to polysulfone tight ultrafiltration hollow fiber membrane. Scientific reports, 10(1), 6880. https://doi.org/10.1038/s41598-020-63356-2
[18] Baig, M. I., Pejman, M., Willott, J. D., Tiraferri, A., & De Vos, W. M. (2022). Polyelectrolyte complex hollow fiber membranes prepared via aqueous phase separation. ACS Applied Polymer Materials, 4(2), 1010-1020. https://doi.org/10.1021/acsapm.1c01464
[19] Kumar, M., Isloor, A. M., Nayak, M. C. S., Todeti, S. R., Padaki, M., & Ismail, A. F. (2023). Hydrophilic polydopamine/polyvinylpyrrolidone blended polyphenylsulfone hollow fiber membranes for the removal of arsenic-V from water. Journal of Environmental Chemical Engineering, 11(5), 110358. https://doi.org/10.1016/j.jece.2023.110358
[20] Vainrot, N., Li, M., Isloor, A. M., & Eisen, M. S. (2021). New preparation methods for pore formation on polysulfone membranes. Membranes, 11(4), 292. https://doi.org/10.3390/membranes11040292
[21] Li, Q., Bi, Q. Y., Lin, H. H., Bian, L. X., & Wang, X. L. (2013). A novel ultrafiltration (UF) membrane with controllable selectivity for protein separation. Journal of Membrane Science, 427, 155-167. https://doi.org/10.1016/j.memsci.2012.06.035
[22] Liang, C. Z., Askari, M., Choong, L. T., & Chung, T. S. (2021). Ultra-strong polymeric hollow fiber membranes for saline dewatering and desalination. Nature communications, 12(1), 2338.
https://doi.org/10.1038/s41467-021-02516-4
[23] Pereira, V. R., Isloor, A. M., Kumar, M., & Ismail, A. F. (2022). Polysulfone nanocomposite membranes containing nanostructured TiO2, SiO2 and 3-Aminopropyltriethoxysilane (APTES) modified nano-TiO2 and nano-SiO2 nanocomposites: Fabrication, characterization and removal of cadmium (Cd2+) ions from aqueous solution. https://doi.org/10.21203/rs.3.rs-1362575/v1
[24] Mansor, H., & Sobran, N. H. M. (2022). Effect of Additive on the Structure and Performance of PVDF Hollow Fibre Membrane on Phosphorus Removal. The Eurasia Proceedings of Science Technology Engineering and Mathematics, 21, 363-370 https://doi.org/10.55549/epstem.1226659
[25] Mithun Kumar, M. K., Rao, T. S., Isloor, A. M., Ibrahim, G. P. S., Inamuddin, I., Norafiqah Ismail, N. I. & Asiri, A. M. (2019). Use of cellulose acetate/polyphenylsulfone derivatives to fabricate ultrafiltration hollow fiber membranes for the removal of arsenic from drinking water. https://doi.org/10.1016/j.ijbiomac.2019.02.017
[26] Rajashekhara, P. H., Isloor, A. M., & Ismail, A. F. (2022). One-step synthesis and characterization of hydrophilic polymer microspheresimmobilized with polyphenylsulfone ultrafiltration membranes for protein rejection application. https://doi.org/10.21203/rs.3.rs-1331802/v1
[27] Han, G., Chung, T. S., Weber, M., & Maletzko, C. (2018). Low-pressure nanofiltration hollow fiber membranes for effective fractionation of dyes and inorganic salts in textile wastewater. Environmental science & technology, 52(6), 3676-3684. https://doi.org/10.1021/acs.est.7b06518
[28] Banjerdteerakul, K., Moghadam, F., Peng, H., & Li, K. (2023). Smoothing the wrinkle formation and improving dye rejection performance in porous graphene oxide membranes using high surface curvature hollow fiber substrates. Journal of Membrane Science, 683, 121763. https://doi.org/10.1016/j.memsci.2023.121763
[29] Kumar, M., Isloor, A. M., Todeti, S. R., Ismail, A. F., & Farnood, R. (2021). Hydrophilic nano-aluminum oxide containing polyphenylsulfone hollow fiber membranes for the extraction of arsenic (As-V) from drinking water. Journal of Water Process Engineering, 44, 102357. https://doi.org/10.1016/j.jwpe.2021.102357
[30] Alijanpour Shalmani, A., Vaezi, A., & Tabatabaei, M. R. (2024). Estimating daily
suspended sediment by intelligent and traditional models (Case Study: Kasalian and Rood Zard watersheds, Iran). Advances in Environmental Technology, 10(2), 102-117. https://doi.org/10.22104/aet.2024.3942
[31] Hamid, M. R. A., & Jeong, H. K. (2018). Recent advances on mixed-matrix membranes for gas separation: Opportunities and engineering challenges. Korean Journal of Chemical Engineering, 35(8), 1577-1600. https://doi.org/10.1007/s11814-018-0081-1
[32] Cao, Z., Barati Farimani, O., Ock, J., & Barati Farimani, A. (2024). Machine learning in membrane design: From property prediction to AI-guided optimization. Nano letters, 24(10), 2953-2960. https://doi.org/10.1021/acs.nanolett.3c05137
[33] Esmaili, Z., Sadeghian, Z., & Ashrafizadeh, S. N. (2024). Tailoring of BiVO4 morphology for efficient antifouling of visible-light-driven photocatalytic ceramic membranes for oily wastewater treatment. Journal of Water Process Engineering, 67, 106145. https://doi.org/10.1016/j.jwpe.2024.106145
[34] Esmaili, Z., Sadeghian, Z., & Ashrafizadeh, S. N. (2023). Anti-fouling and self-cleaning ability of BiVO4/rGO and BiVO4/g-C3N4 visible light-driven photocatalysts modified ceramic membrane in high performance ultrafiltration of oily wastewater. Journal of Membrane Science, 688, 122147. https://doi.org/10.1016/j.memsci.2023.122147
[35]Karimzadeh, M., Khatibi, M., & Ashrafizadeh, S. N. (2022). Boost ionic selectivity by coating bullet-shaped nanochannels with dense polyelectrolyte brushes. Physics of Fluids, 34(12). https://doi.org/10.1063/5.0130425
[36] Khatibi, M., & Ashrafizadeh, S. N. (2024). Mitigating Joule heating in smart nanochannels: Evaluating the efficacy of AC vs. DC fields. International Communications in Heat and Mass Transfer, 154, 107448. https://doi.org/10.1016/j.icheatmasstransfer.2024.107448
[37] Khatibi, M., & Ashrafizadeh, S. N. (2023). Ion transport in intelligent nanochannels: A comparative analysis of the role of electric field. Analytical Chemistry, 95(49), 18188-18198. https://doi.org/10.1021/acs.analchem.3c03809
[38] Khatibi, M., Aminnia, A., & Ashrafizadeh, S. N. (2024). The role of ionic concentration polarization on the behavior of nanofluidic membranes. Chemical Engineering and Processing-Process Intensification, 202, 109849. https://doi.org/10.1016/j.cep.2024.109849
[39] Hoshyargar, V., Ashrafizadeh, S. N., & Sadeghi, A. (2015). Drastic alteration of diffusioosmosis due to steric effects. Physical Chemistry Chemical Physics, 17(43), 29193-29200. https://doi.org/10.1039/C5CP05327G
[40] Dartoomi, H., Khatibi, M., & Ashrafizadeh, S. N. (2022). Enhanced ionic current rectification through innovative integration of polyelectrolyte bilayers and charged-wall smart nanochannels. Analytical Chemistry, 95(2), 1522-1531. https://doi.org/10.1021/acs.analchem.2c04559
[41] Jafari, S., Khatibi, M., & Ashrafizadeh, S. N. (2024). Blue energy conversion utilizing smart ionic nanotransistors. Electrochimica Acta, 507, 145186. https://doi.org/10.1016/j.electacta.2024.145186
[42]Khatibi, M., Mehta, S. K., Ashrafizadeh, S. N., & Mondal, P. K. (2024). Surface charge-dependent slip length modulates electroosmotic mixing in a wavy micromixer. Physics of Fluids, 36(7). https://doi.org/10.1063/5.0218566
[43] Qi, H., Sun, W., Zhao, S., Zhao, Z., Sun, Y., Zhu, Y., Mu, J., Zhang, H., Zhu, X., Jiang, Z., & Jiang, L. (2024). Permeability and selectivity synergistically enhanced nanofluidic membrane for osmotic energy harvesting. Carbon Energy. https://doi.org/10.1002/cey2.458
[44] Kitto, D., & Kamcev, J. (2024). Predicting the conductivity–selectivity trade-off and upper bound in ion-exchange membranes. ACS Energy Letters, 9(4), 1346-1352. https://doi.org/10.1021/acsenergylett.4c00301
[45] Karimzadeh, M., Khatibi, M., & Ashrafizadeh, S. N. (2022). Boost ionic selectivity by coating bullet-shaped nanochannels with dense polyelectrolyte brushes. Physics of Fluids, 34(12). https://doi.org/10.1063/5.0130425
[46] Alinezhad, A., Khatibi, M., & Ashrafizadeh, S. N. (2023). Ionic transfer behavior of bipolar nanochannels resembling PNP nanotransistor. Electrochimica Acta, 460, 142625. https://doi.org/10.1016/j.electacta.2023.142625
[47] Prabhu, S., Prasad, K., Robels-Kelly, A., & Lu, X. (2022). AI-based carcinoma detection and classification using histopathological images: A systematic review. Computers in Biology and Medicine, 142, 105209. https://doi.org/10.1016/j.compbiomed.2022.105209
[48] Haddaway, N. R., Page, M. J., Pritchard, C. C., & McGuinness, L. A. (2022). PRISMA2020: An R package and Shiny app for producing PRISMA 2020‐compliant flow diagrams, with interactivity for optimised digital transparency and Open Synthesis. Campbell systematic reviews, 18(2), e1230. https://doi.org/10.1002/cl2.1230.
[49] Karki, S., Hazarika, G., Yadav, D., & Ingole, P. G. (2024). Polymeric membranes for industrial applications: Recent progress, challenges and perspectives. Desalination, 573, 117200. https://doi.org/10.1016/j.desal.2024.117200
[50] Zhao, S., Minier-Matar, J., Chou, S., Wang, R., Fane, A. G., & Adham, S. (2017). Gas field produced/process water treatment using forward osmosis hollow fiber membrane: Membrane fouling and chemical cleaning. Desalination, 402, 143-151. https://doi.org/10.1016/j.desal.2016.10.006
[51] Zhang, J., Xie, M., Tong, X., Yang, D., Liu, S., Qu, D., Feng, L., & Zhang, L. (2021). Ammonia capture from human urine to harvest liquid N-P compound fertilizer by a submerged hollow fiber membrane contactor: Performance and fertilizer analysis. Science of The Total Environment, 768, 144478. https://doi.org/10.1016/j.scitotenv.2020.144478
[52] Wang, X., Ping, W., & Al-Shati, A. S. (2023). Numerical simulation of ozonation in hollow-fiber membranes for wastewater treatment. Engineering Applications of Artificial Intelligence, 123, 106380. https://doi.org/10.1016/j.engappai.2023.106380
[53] Rutten, S. B., Junker, M. A., Leal, L. H., de Vos, W. M., Lammertink, R. G., & de Grooth, J. (2023). Influence of dominant salts on the removal of trace micropollutants by hollow fiber nanofiltration membranes. Journal of Membrane Science, 678, 121625. https://doi.org/10.1016/j.memsci.2023.121625
[54] da Silva Biron, D., Espindola, J. C., Subtil, E. L., & Mierzwa, J. C. (2023). A new approach to the development of hollow fiber membrane modules for water treatment: mixed polymer matrices. Membranes, 13(7), 613. https://doi.org/10.3390/membranes13070613
[55] Gaudio, M. T., Curcio, S., & Chakraborty, S. (2023). Design of an integrated membrane system to produce dairy by-product from waste processing. International Journal of Food Science and Technology, 58(4), 2104-2114. https://doi.org/10.1111/ijfs.15986
[56] Sergey, B., Pavel, N., Andrey, B., Vladimir, Z., Dmitriy, M., & Alexandr, M. (2018). Hydrodynamics and mass transfer with gel formation in a roll type ultrafiltration membrane. Foods and Raw materials, 6(2), 350-357. https://doi.org/10.21603/2074-9414-2018-6-2-350-357
[57] Poudineh, M. T., Zarafshan, P., Mirsaeedghazi, H., & Dehghani, M. (2019). Comparison study of the effect modeling of flow parameters on the membrane clarification efficiency for pomegranate juice. Engineering in Agriculture, Environment and Food, 12(4), 379-387. https://www.sciencedirect.com/science/article/abs/pii/S1881836619300096
[58] Nejad, A. R. S., Ghaedi, A. M., Madaeni, S. S., Baneshi, M. M., Vafaei, A., Emadzadeh, D., & Lau, W. J. (2019). Development of intelligent system models for prediction of licorice concentration during nanofiltration/reverse osmosis process. Desalination and Water Treatment, 145, 83-95. https://doi.org/10.5004/dwt.2019.23731
[59] Vatanpour, V., Nekouhi, G. N., & Esmaeili, M. (2020). Preparation, characterization and performance evaluation of ZnO deposited polyethylene ultrafiltration membranes for dye
and protein separation. Journal of the Taiwan Institute of Chemical Engineers, 114, 153-167. https://doi.org/10.1016/j.jtice.2020.09.008
[60] Trombino, S., Sole, R., Curcio, F., & Cassano, R. (2023). Polymeric based hydrogel membranes for biomedical applications. Membranes, 13(6), 576. https://doi.org/10.3390/membranes13060576
[61] Chen, Z., Lv, Z., Sun, Y., Chi, Z., & Qing, G. (2020). Recent advancements in polyethyleneimine-based materials and their biomedical, biotechnology, and biomaterial applications. Journal of Materials Chemistry B, 8(15), 2951-2973. https://doi.org/10.1039/C9TB02271F
[62] An, Z., Xu, R., Dai, F., Xue, G., He, X., Zhao, Y., & Chen, L. (2017). PVDF/PVDF-g-PACMO blend hollow fiber membranes for hemodialysis: preparation, characterization, and performance. RSC Advances, 7(43), 26593-26600. https://doi.org/10.1039/C7RA03366D
[63] Miller, J. J., Carter, J. A., Hill, K., DesOrmeaux, J. P. S., Carter, R. N., Gaborski, T. R. & Johnson, D. G. (2020). Free standing, large-area silicon nitride membranes for high toxin clearance in blood surrogate for small-format hemodialysis. Membranes, 10(6), 119. https://doi.org/10.3390/membranes10060119
[64] Alshammari, N., Alazmi, M., & Veettil, V. N. (2021). Applying a Hydrophilic Modified Hollow Fiber Membrane to Reduce Fouling in Artificial Lungs. Separations, 8(8), 113. https://doi.org/10.3390/separations8080113
[65] Tang, Y. S., Tsai, Y. C., Chen, T. W., & Li, S. Y. (2022). Artificial kidney engineering: the development of dialysis membranes for blood purification. Membranes, 12(2), 177. https://doi.org/10.3390/membranes12020177
[66] Fang, F., Zhao, H. Y., Wang, R., Chen, Q., Wang, Q. Y., & Zhang, Q. H. (2023). Fabrication and Study of Dextran/Sulfonated Polysulfone Blend Membranes for Low-Density Lipoprotein Adsorption. Materials, 16(13), 4641. https://doi.org/10.3390/ma16134641
[67] Rutten, S. B., Junker, M. A., Leal, L. H., de Vos, W. M., Lammertink, R. G., & de Grooth, J. (2023). Influence of dominant salts on the removal of trace micropollutants by hollow fiber nanofiltration membranes. Journal of Membrane Science, 678, 121625. https://doi.org/10.1016/j.memsci.2023.121625
[68] Bethi, B., Sonawane, S. H., Bhanvase, B. A., & Sonawane, S. S. (2021). Textile industry wastewater treatment by cavitation combined with fenton and ceramic nanofiltration membrane. Chemical Engineering and Processing-Process Intensification, 168, 108540. https://doi.org/10.1016/j.cep.2021.108540
[69] Hebbar, R. S., Isloor, A. M., Zulhairun, A. K., Abdullah, M. S., & Ismail, A. F. (2017). Efficient treatment of hazardous reactive dye effluents through antifouling polyetherimide hollow fiber membrane embedded with functionalized halloysite nanotubes. Journal of the Taiwan Institute of Chemical Engineers,72, 244-252. https://doi.org/10.1016/j.jtice.2017.01.022
[70] Liu, H., Chen, Y., Zhang, K., Wang, C., Hu, X., Cheng, B., & Zhang, Y. (2019). Poly (vinylidene fluoride) hollow fiber membrane for high-efficiency separation of dyes-salts. Journal of Membrane Science, 578, 43-52. https://doi.org/10.1016/j.memsci.2019.02.029
[71] Maleš, L., Fakin, D., Bračič, M., & Gorgieva, S. (2020). Efficiency of differently processed membranes based on cellulose as cationic dye adsorbents. Nanomaterials, 10(4), 642. https://doi.org/10.3390/nano10040642
[72] Cockerham, C., Caruthers, A., McCloud, J., Fortner, L. M., Youn, S., & McBride, S. P. (2022). Azo-dye-functionalized polycarbonate membranes for textile dye and nitrate ion removal. Micromachines, 13(4), 577. https://doi.org/10.3390/mi13040577
[73] Ahmad, H., Zahid, M., Rehan, Z. A., Rashid, A., Akram, S., Aljohani, M. M., & Al-Harbi, M. S. (2022). Preparation of polyvinylidene fluoride nano-filtration membranes modified with functionalized graphene oxide for textile dye removal. Membranes, 12(2), 224. https://doi.org/10.3390/membranes12020224
[74]Zulfiani, U., Junaidi, A., Nareswari, C., Ali, B. T. I., Jaafar, J., Widyanto, A. R.,... & Widiastuti, N. (2023). Performance of a membrane fabricated from high-density polyethylene
waste for dye separation in water. RSC advances, 13(12), 7789-7797. https://doi.org/10.1039/D2RA07595D
[75] Bazhenov, S. D., Bildyukevich, A. V., & Volkov, A. V. (2018). Gas-liquid hollow fiber membrane contactors for different applications. Fibers, 6(4), 76. https://doi.org/10.3390/fib6040076
[76] Bernardo, P., Tasselli, F., & Clarizia, G. (2019). Gas separation Hollow Fiber Membranes: Processing conditions for manipulating morphology and performance. CHEMICAL ENGINEERING, 74. https://doi.org/10.1016/j.cep.2019.06.007
[77] Ecker, P., Pekovits, M., Yorov, T., Haddadi, B., Lukitsch, B., Elenkov, M., & Harasek, M. (2021). Microstructured Hollow Fiber Membranes: Potential Fiber Shapes for Extracorporeal Membrane Oxygenators. Membranes, 11(5), 374. https://doi.org/10.3390/membranes11050374
[78] Sharma, A. K., Conover, S. P., & Sirkar, K. K. (2022). Plasma polymerized coatings on hollow fiber membranes-applications and their aging characteristics in different media. Membranes, 12(7), 656. https://doi.org/10.3390/membranes12070656
[79] Sohaib, Q., Kalakech, C., Charmette, C., Cartier, J., Lesage, G., & Mericq, J. P. (2022). Hollow-fiber membrane contactor for biogas recovery from real anaerobic membrane bioreactor permeate. Membranes, 12(2), 112. https://doi.org/10.3390/membranes12020112
[80] González-Revuelta, D., Fallanza, M., Ortiz, A., & Gorri, D. (2023). Thin-film composite matrimid-based hollow fiber membranes for oxygen/nitrogen separation by gas permeation. Membranes, 13(2), 218. https://doi.org/10.3390/membranes13020218
[81] Shukla, A. K., Alam, J., & Alhoshan, M. (2022). Recent advancements in polyphenylsulfone membrane modification methods for separation applications. Membranes, 12(2), 247. https://doi.org/10.3390/membranes12020247
[82] Okamoto, Y., Chiang, H. C., Fang, M., Galizia, M., Merkel, T., Yavari, M., & Lin, H. (2020). Perfluorodioxolane polymers for gas separation membrane applications. Membranes, 10(12), 394. https://doi.org/10.3390/membranes10120394
[83] Asghari, Z., Arasteh, B., & Koochari, A. (2023). Effective software mutation-test using program instructions classification. Journal of Electronic Testing, 39(5), 631-657. https://doi.org/10.1007/s10836-023-06089-0
[84] Talukder, M. J., Alshami, A. S., Tayyebi, A., Ismail, N., & Yu, X. (2024). Membrane science meets machine learning: future and potential use in assisting membrane material design and fabrication. Separation & Purification Reviews, 53(2), 216-229.https://doi.org/10.1080/15422119.2023.2212295
[85] Tayyebi, A., Alshami, A. S., Yu, X., & Kolodka, E. (2022). Can machine learning methods guide gas separation membranes fabrication? Journal of Membrane Science Letters, 2(2), 100033. https://doi.org/10.1016/j.msl.2022.100033.
[86] Ritt, C. L., Liu, M., Pham, T. A., Epsztein, R., Kulik, H. J., & Elimelech, M. (2022). Machine learning reveals key ion selectivity mechanisms in polymeric membranes with subnanometer pores. Science advances, 8(2), eabl5771. https://www.science.org/doi/10.1126/sciadv.abl5771
[87] Tao, L., Varshney, V., & Li, Y. (2021). Benchmarking machine learning models for polymer informatics: an example of glass transition temperature. Journal of Chemical Information and Modeling, 61(11), 5395-5413. https://doi.org/10.1021/acs.jcim.1c01031
[88] Jasim, D. J., Mohammed, T. J., Harharah, H. N., Harharah, R. H., Amari, A., & Abid, M. F. (2023). Modeling and optimal operating conditions of hollow fiber membrane for CO2/CH4 separation. Membranes, 13(6), 557. https://doi.org/10.3390/membranes13060557
[89] Wang, M., Xu, Q., Tang, H., & Jiang, J. (2022). Machine learning-enabled prediction and high-throughput screening of polymer membranes for pervaporation separation. ACS applied materials & interfaces, 14(6), 8427-8436. https://doi.org/10.1021/acsami.1c22886
[90] Glass, S., Schmidt, M., Merten, P., Abdul Latif, A., Fischer, K., Schulze, A.,... & Filiz, V. (2024). Design of modified polymer membranes using machine learning. ACS Applied Materials & Interfaces, 16(16), 20990-21000. https://doi.org/10.1021/acsami.3c18805
[91] Kim, N. E., Basak, J. K., & Kim, H. T. (2023). Application of Hollow Fiber Membrane for the Separation of Carbon Dioxide from Atmospheric Air and Assessment of Its Distribution Pattern in a Greenhouse. Atmosphere, 14(2), 299. https://doi.org/10.3390/atmos14020299
[92] Yang, J., Tao, L., He, J., McCutcheon, J. R., & Li, Y. (2022). Machine learning enables interpretable discovery of innovative polymers for gas separation membranes. Science Advances, 8(29), eabn9545.
https://www.science.org/doi/10.1126/sciadv.abn9545
[93] Galinha, C. F., & Crespo, J. G. (2021). From black box to machine learning: A journey through membrane process modelling. Membranes, 11(8), 574. https://doi.org/10.3390/membranes11080574
[94] Bestwick, T., Beckmann, J., & Camarda, K. V. (2023). Using Artificial Neural Networks to Predict Physical Properties of Membrane Polymers. Chemie Ingenieur Technik, 95(3), 363-367. https://doi.org/10.1002/cite.202200102
[95] Barnett, J. W., Bilchak, C. R., Wang, Y., Benicewicz, B. C., Murdock, L. A., Bereau, T., & Kumar, S. K. (2020). Designing exceptional gas-separation polymer membranes using machine learning. Science advances, 6(20), eaaz4301. https://www.science.org/doi/full/10.1126/sciadv.aaz4301
[96] Tao, L., Varshney, V., & Li, Y. (2021). Benchmarking machine learning models for polymer informatics: an example of glass transition temperature. Journal of Chemical Information and Modeling, 61(11), 5395-5413.https://pubs.acs.org/doi/10.1021/acs.jcim.1c01031
[97] Gao, H., Zhong, S., Dangayach, R., & Chen, Y. (2023). Understanding and designing a high-performance ultrafiltration membrane using machine learning. Environmental Science & Technology, 57(46), 17831-17840. https://doi.org/10.1021/acs.est.2c05404
[98] Alwatban, A. M., Alshwairekh, A. M., Alqsair, U. F., Alghafis, A. A., & Oztekin, A. (2019). Effect of membrane properties and operational parameters on systems for seawater desalination using computational fluid dynamics simulations. Desalination and Water Treatment, 161, 92-107. https://doi.org/10.5004/dwt.2019.24275
[99] Nguyen, X. L., Trinh, N. V., Kim, Y., & Yu, S. (2022). A correlation of overall mass transfer coefficient of water transport in a hollow-fiber membrane module via an artificial neural network approach. Membranes, 13(1), 8. https://doi.org/10.3390/membranes13010008 B. Anupama / Advances in Environmental Technology 12(1) 2026, 32-62. 61
[100] Stel’makh, S. A., Shcherban’, E. M., Beskopylny, A. N., Mailyan, L. R., Meskhi, B., Razveeva, I.& Beskopylny, N. (2022). Prediction of mechanical properties of highly functional lightweight fiber-reinforced concrete based on deep neural network and ensemble regression trees methods. Materials, 15(19), 6740. https://doi.org/10.3390/ma15196740
[101] Shahouni, R., Abbasi, M., Dibaj, M., & Akrami, M. (2024). Utilising artificial intelligence to predict membrane behaviour in water purification and desalination. Water, 16(20), 2940. https://doi.org/10.3390/w16202940
[102] Ismael, B., Khaleel, F., Ibrahim, S. S., Khaleel, S. R., AlOmar, M. K., Masood, A., Aljumaily, M. M., Alsalhy, Q. F., Razali, S. F. M., Al-Juboori, R. A., Hameed, M. M., & Alsarayreh, A. A. (2023). Permeation flux prediction of vacuum membrane distillation using hybrid machine learning techniques. Membranes, 13(12), 900. https://doi.org/10.3390/membranes13120900
[103] Liu, T., Liu, L., Cui, F., Ding, F., Zhang, Q., & Li, Y. (2020). Predicting the performance of polyvinylidene fluoride, polyethersulfone and polysulfone filtration membranes using machine learning. Journal of Materials Chemistry A, 8(41), 21862-21871. https://doi.org/10.1039/D0TA07607D
[104] Fetanat, M., Keshtiara, M., Keyikoglu, R., Khataee, A., Daiyan, R., & Razmjou, A. (2021). Machine learning for design of thin-film nanocomposite membranes. Separation and Purification Technology, 270, 118383. https://doi.org/10.1016/j.seppur.2021.118383
[105] Salehi, R., Krishnan, S., Nasrullah, M., & Chaiprapat, S. (2023). Using Machine learning to predict the performance of a cross-flow ultrafiltration membrane in xylose reductase separation. Sustainability, 15(5), 4245.https://doi.org/10.3390/su15054245
[106] Waqas, S., Harun, N. Y., Sambudi, N. S., Arshad, U., Nordin, N. A. H. M., Bilad, M. R.,... & Malik, A. A. (2022). SVM and ANN modelling approach for the optimization of membrane permeability of a membrane rotating biological contactor for wastewater treatment. Membranes, 12(9), 821. https://doi.org/10.3390/membranes12090821
[107] Kovacs, D. J., Li, Z., Baetz, B. W., Hong, Y., Donnaz, S., Zhao, X., & Dong, Q. (2022).
Membrane fouling prediction and uncertainty analysis using machine learning: A wastewater treatment plant case study. Journal of Membrane Science, 660, 120817. https://doi.org/10.1016/j.memsci.2022.120817
[108] Zadkarami, M., Safavi, A. A., Gernaey, K. V., Ramin, P., & Prado-Rubio, O. A. (2023). Designing a fault detection classifier framework for an industrial dynamic ultrafiltration membrane process using wavelet-based feature analysis. Process Safety and Environmental Protection, 174, 1-19. https://doi.org/10.1016/j.psep.2023.04.007
[109] Shim, J., Hong, S., Lee, J., Lee, S., Kim, Y. M., Chon, K., & Cho, K. H. (2023). Deep learning with data preprocessing methods for water quality prediction in ultrafiltration. Journal of Cleaner Production, 428, 139217. https://doi.org/10.1016/j.jclepro.2023.139217
[110] Tanudjaja, H. J., Ng, A. Q. Q., & Chew, J. W. (2023). Understanding single-protein fouling in micro-and ultrafiltration systems via machine-learning-based models. Industrial & Engineering Chemistry Research, 62(19), 7610-7621. https://doi.org/10.1021/acs.iecr.2c03205
[111] Kellermann, C., Selmi, A., Brown, D., & Ostermann, J. (2022, July). Fault detection in multi-stage manufacturing to improve process quality. In 2022 International Conference on Control, Automation and Diagnosis (ICCAD) (pp. 1-6). IEEE. https://doi.org/10.1109/ICCAD55197.2022.9853909
[112] Mir, T. A., Banerjee, D., Malhotra, S., & Devliyal, S. (2024, July). Advancements in Quality Control: Integrating Machine Learning for Defect Prediction. In 2024 2nd World Conference on Communication & Computing (WCONF) (pp. 1-6). IEEE. https://doi.org/10.1109/wconf61366.2024.10692222
[113] Scheinker, A. (2021). Adaptive machine learning for robust diagnostics and control of time-varying particle accelerator components and beams. Information, 12(4), 161. https://doi.org/10.3390/info12040161
[114] Ramel, A. A. (2022). Analysis of membrane process model from black box to machine learning. Journal of Machine and Computing, 1-8. https://doi.org/10.53759/7669/jmc202202001
[115] Mukherjee, A., Adeyemo, S., & Bhattacharyya, D. (2025). All‐nonlinear static‐dynamic neural networks versus Bayesian machine learning for data‐driven modelling of chemical processes. The Canadian Journal of Chemical Engineering, 103(3), 1139-1154. https://doi.org/10.1002/cjce.25379
[116] Demir, H., & Keskin, S. (2024). A New Era of Modeling MOF‐Based Membranes: Cooperation of Theory and Data Science. Macromolecular Materials and Engineering, 309(1), 2300225. https://doi.org/10.1002/mame.202300225
[117] Jasthi, B. K., Gadhamshetty, V., Sereda, G. A., & Lipatov, A. (2023). 11 Machine Learning
for. Machine Learning in 2D Materials Science, 201. https://doi.org/10.1201/9781003132981-11
[118] Anselmi, L., & Rognoli, V. (2023). Emerging materials fostering interdisciplinary collaboration in Materials Design. In Interdisciplinary Practice in Industrial Design (AHFE Open Access),119-129. https://doi.org/10.54941/ahfe1002978
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