Suchergebnisse

The electrospinning of high-performance polyimides (PI) has recently sparked great interest. In this study, we explore the effect of the electrospinning parameters — namely polymer concentration, voltage, tip-to-collector distance and flow rate — and salt addition on the diameter, morphology, and spinnability of electrospun PI nanofibers. Three different polyimides of intrinsic microporosity (PIM-PIs) with high Brunauer–Emmett–Teller (BET) ranging from 270 to 506 m2 g−1, and two microporous polyimides, were synthesized through the polycondensation of 4,4′-(hexafluoroisopropylidene)diphthalic anhydride (6FDA) and aromatic diamines. The addition of tetraethylammonium bromide (TEAB) salt considerably increased the conductivity of all the PI solutions, significantly improved spinability, and resulted in thinner fibers. We also used molecular dynamic simulations to investigate the macromolecular mechanism of improved spinnability and fiber morphology in the presence of an ammonium salt. The small droplets detached from the parent droplet, followed by the rapid evaporation of the ions through the hydration effect, which facilitated the electrospinning. The resulting uniform nanofibers have great potential in environmental applications due to the presence of microporosity and hydrophobic pendant trifluoromethyl groups, which enhance the sorption performance of the fibers for hydrophobic species. ; The postdoctoral fellowship from King Abdullah University of Science and Technology (KAUST) is gratefully acknowledged (FT). The research reported in this publication was supported by funding from KAUST. This work was supported by the VEKOP-2.1.1-15-2016-00114 project, which is co-financed by the Hungarian Government and the European Union.

There is an urgent need to develop predictive methodologies that will fast-track the industrial implementation of organic solvent nanofiltration (OSN). However, the performance prediction of OSN membranes has been a daunting and challenging task, due to the high number of possible solvents and the complex relationship between solvent-membrane, solute-solvent, and solute-membrane interactions. Therefore, instead of developing fundamental mathematical equations, we have broken away from conventions by compiling a large dataset and building artificial intelligence (AI) based predictive models for both rejection and permeance, based on a collected dataset containing 38,430 datapoints with more than 18 dimensions (parameters). To elucidate the important parameters that affect membrane performance, we have carried out a thorough principal component analysis (PCA), which revealed that the factors affecting both permeance and rejection are surprisingly similar. We have trained three different AI models (artificial neural network, support vector machine, random forest) that predicted the membrane performance with unprecedented accuracy, as high as 98% (permeance) and 91% (rejection). Our findings pave the way towards appropriate data standardization, not only for performance prediction, but also for better membrane design and development. ; The authors thank Murielle Rabiller-Baudry from Université Rennes; Anja Drews from HTW Berlin, Yvonne Thiermeyer from Merck KGaA and TU Dortmund; Stefanie Blumenschein from Merck KGaA and TU Dortmund; Matthias Wessling from RWTH Aachen; Dominic Ormerod from VITO; and Gregory S. Smith from University of Cape Town for the provision of data related to their published articles. The PhD scholarship from King Abdullah University of Science and Technology (KAUST) is gratefully acknowledged (JH). The research reported in this publication was supported by funding from KAUST. JFK thanks the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2019M3E6A1064799, 2019R1G1A109477811, and 2020R1C1C1007876). CSK and JYK thanks the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (No. 2019R1F1A106365312).

Membrane technology has become an indispensable part of our daily lives. The rapid growth of membrane technology has been breeding an unavoidable yet critical challenge─the unsustainable disposal of used membranes. Commercial polymer membranes are fabricated from fossil-based monomers and polymers that are not biodegradable. Hence, there is an urgent need to develop membranes that are sustainable from cradle to grave, i.e., both bioderived and biodegradable. Cellulose is one of the most abundant biopolymers that are biodegradable upon disposal. However, it is only soluble in a handful of solvents, limiting its fabrication into membranes at an industrial scale. To circumvent this bottleneck, in this work, we propose a sustainable and scalable method to fabricate cellulose membranes from cellulose acetate with a sacrificial acetate group. The proposed method allows cellulose membrane fabrication utilizing green solvents, and the fabrication procedure is sustainable with minimal solvent consumption. One of the most appealing applications of cellulose membranes is organic solvent nanofiltration (OSN). It is an emerging technology to separate solutes in nanoprecision in harsh organic solvents, requiring solvent-stable materials. Surprisingly, the cellulose membranes exhibited unique transport behaviors, with solute rejection ranging from 100 to −100% depending on the solvent medium. Such trends were not previously observed in the OSN literature, and the underlying mechanism was thoroughly investigated. Importantly, the membranes were completely biodegradable in a carbon-neutral manner upon disposal. The life cycle of cellulose membranes was compared with that of conventional OSN membranes in a qualitative and comparative study. The proposed methodology can be applied to substitute fossil-based polymers in all aspects of membrane technology, and it has the potential to become a sustainable fabrication platform for membrane materials. ; This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (Nos. 2020R1C1C100787612 and 2021M3H4A1A0409288511). The postdoctoral fellowship from the King Abdullah University of Science and Technology (KAUST) is gratefully acknowledged (S.K.). The research reported in this publication was supported by funding from KAUST.