Browsing by Author "Chen, Yongsheng"

Now showing 1 - 3 of 3

Enhanced hydrophilicity and mechanical robustness of polysulfone nanofiber membranes by addition of polyethyleneimine and Al2O3 nanoparticles
(ELSEVIERRADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS, 2017) Uzal, Nigmet; Ates, Nuray; Saki, Seda; Bulbul, Y. Emre; Chen, Yongsheng; AGÜ, Mühendislik Fakültesi, İnşaat Mühendisliği Bölümü; Uzal, Nigmet
A novel hydrophilic and mechanically robust polysulfone (PSF) nanofiber membrane (NFM) was prepared by electrospinning of a PSF solution blended with polyethyleneimine (PEI) and Al2O3 nanoparticles. The influence of PEI and Al2O3 nanoparticles concentration on the NFM characteristics was studied using scanning electron microscopy (SEM), Fourier transform infrared FT-IR spectroscopy, porosity, water contact angle measurement, and tensile strength test. Filtration performance of the nanofiber membranes (NFMs) were evaluated by the measurement of pure water flux (PWF) and bovine serum albumin (BSA) rejection tests. According to the results, blending PSF solution with 2 wt.% PEI and 0.05 wt.% Al2O3 nanoparticles resulted in formation of NFMs with high porosity and increased mechanical strength, which exhibited a low water contact angle of 23.5 and high water flux of 28,456 L/m(2) h. On the other hand, incorporation of nanoparticles and PEI in the PSF membrane matrix led to increasing of tensile strength that it was changed from 0.15 to 0.69 for pure PSF and PSF/PEI/Al2O3, respectively. A-24 and 48% BSA rejection performances were obtained by nanoparticle incorporated PSF membranes. In conclusion, the studies strongly suggest that blending with hydrophilic additives of NFMs can enhance the hydrophilicity and mechanical strength of PSF membranes and these NFMs can be effectively used in water based membrane systems. (C) 2017 Elsevier B.V. All rights reserved.
Machine Learning-Aided Inverse Design and Discovery of Novel Polymeric Materials for Membrane Separation
(ACS Publications, 2024) Dangayach, Raghav; Jeong, Nohyeong; Demirel, Elif; Uzal, Nigmet; Fung, Victor; Chen, Yongsheng; 0000-0002-0912-3459; AGÜ, Mühendislik Fakültesi, İnşaat Mühendisliği Bölümü; Uzal, Nigmet
Polymeric membranes have been widely used for liquid and gas separation in various industrial applications over the past few decades because of their exceptional versatility and high tunability. Traditional trial-and-error methods for material synthesis are inadequate to meet the growing demands for high-performance membranes. Machine learning (ML) has demonstrated huge potential to accelerate design and discovery of membrane materials. In this review, we cover strengths and weaknesses of the traditional methods, followed by a discussion on the emergence of ML for developing advanced polymeric membranes. We describe methodologies for data collection, data preparation, the commonly used ML models, and the explainable artificial intelligence (XAI) tools implemented in membrane research. Furthermore, we explain the experimental and computational validation steps to verify the results provided by these ML models. Subsequently, we showcase successful case studies of polymeric membranes and emphasize inverse design methodology within a ML-driven structured framework. Finally, we conclude by highlighting the recent progress, challenges, and future research directions to advance ML research for next generation polymeric membranes. With this review, we aim to provide a comprehensive guideline to researchers, scientists, and engineers assisting in the implementation of ML to membrane research and to accelerate the membrane design and material discovery process.
Potential ion exchange membranes and system performance in reverse electrodialysis for power generation: A review
(ELSEVIER, 2015) Hong, Jin Gi; Zhang, Bopeng; Glabman, Shira; Uzal, Nigmet; Dou, Xiaomin; Zhang, Hongguo; Wei, Xiuzhen; Chen, Yongsheng; 0000-0002-0912-3459; AGÜ, Mühendislik Fakültesi, İnşaat Mühendisliği Bölümü; Uzal, Nigmet
Reverse electrodialysis (RED) is an emerging membrane-based energy conversion process used to extract electricity by mixing two water streams of different salinities. This technique utilizes transport of cations and anions during controlled mixing of saltwater and freshwater through selective ion exchange membranes. The development of ion exchange membranes and optimization of system performance are crucial for sustainable energy capture from salinity gradients using RED. Recently, increased attention has been given to the preparation of ion exchange membranes and to understanding the factors that determine the RED power performance. This review evaluates potential ion exchange membrane materials, currently available state-of-the-art RED membranes, and their key properties. Discussion will focus on the electrochemical and physical properties of these membranes (e.g., resistance, permselectivity, and swelling) because of their significant role in RED performance throughout the system. Although an interconnected relationship exists between membrane properties, RED requires high quality membranes that are uniquely tailored to have a low resistance and high permselectivity. Moreover, harnessing this potential technology demands not only carefully optimized components but also a novel RED stack design and system optimization. The key findings and advancements needed to assure proper stack design and optimization are also described. This review paper[U+05F3]s goal is to elucidate effective energy conversion from salinity gradients and expedite implementation of RED as the next promising renewable source of power for large-scale energy generation.