Modelling of the separation performance and electrokinetic properties of nanofiltration membranes

Flexible Covalent Organic Network with Ordered Honeycomb Nanoarchitecture for Molecular Separations

Membranes with precisely defined nanostructure are desirable for energy-efficient molecular separations. The emergence of membranes with honeycomb lattice or topological nanopores is of fundamental importance. The tailor-made nanostructure and morphology may have huge potential to resolve the longstanding bottlenecks in membrane science and technology. Herein, inspired by honeycomb architecture, we demonstrate an effective and scalable route based on interfacial polymerization (IP) to generate flexible and ordered covalent organic network (CON) membranes for liquid-phase molecular separations. The aperture size of a CON membrane can be reasonably designed through the strong covalent bond between molecular building blocks. The fabricated CON membrane formed by IP showed an obviously size-dependent sieving of molecules, yielding a stepwise conversion from low rejection to the expected high rejection. Moreover, the CON membrane was also found to have the sieving capability for tetracycline and ciprofloxacin, ascribed to the effect of size exclusion by an ordered single-nanoscale channel (<1 nm). This approach provides a viable strategy for creating target-sized channels from molecular-level design and demonstrates their potential for accurate molecular separations.

A super-hydrophilic partially reduced graphene oxide membrane with improved stability and antibacterial properties

In order to improve stability and antibacterial property, a novel super-hydrophilic partially reduced graphene oxide membrane was prepared by interfacial polymerization of piperazine and partially reduced graphene oxide as aqueous solution and trimesoyl chloride as organic solution. Fourier transform infrared spectroscopy, scanning electron microscope, and contact angle measurement were conducted to probe the morphology and properties of the membranes. The modified membrane possessed super-hydrophilicity, improved durability and swelling resistance. The optimized membrane had a molecular weight cut off of about 674 Da and possessed a pure water permeability of 49.86 L·m^-2·h^-1·MPa^-1. The retention order of salts was Na₂SO₄ > MgSO₄ > MgCl₂ > Na₂CO₃ > CaCl₂ > NaCl, while the rejection for four kinds of pharmaceuticals followed the order of ibuprofen (92%) > carbamazepine (87%) > amlodipine (80%) > atenolol (76%), indicating that the negatively charged membrane could improve the retention performance by the electrostatic repulsive effect. Moreover, the enhanced antibacterial performance of membrane attributed to the dual effects of the super-hydrophilicity and the tea polyphenols antibacterial material loading, which may alter the charge distribution on and within the membrane, leading to loss of cell viability.

Dependable Performance of Thin Film Composite Nanofiltration Membrane Tailored by Capsaicin-Derived Self-Polymer

To address trade-off and membrane-fouling challenges during the development of nanofiltration membranes, a thin-film composite membrane was prepared on the basis of interfacial polymerization regulated by adjusting the capsaicin-derived self-polymer poly N-(2-hydroxy-5-(methylthio) benzyl) acrylamide (PHMTBA) on the polysulfone substrate in this study. Through the self-polymerization of the monomer HMTBA with varied contents, microwave-assisted technology was employed to develop a variety of PHMTBAs. It was discovered that PHMTBA is involved in the interfacial polymerization process. Piperazine and PHMTBA competed for the reaction with trimesoyl chloride, resulting in a flatter and looser membrane surface. The PHMTBA-modified membrane presented a typical double-layer structure: a thicker support layer and a thinner active layer. The addition of PHMTBA to membranes improved their hydrophilicity and negative charge density. As a result, the PHMTBA-modified membrane showed dependable separation performance (water flux of 159.5 L m⁻² h⁻¹ and rejection of 99.02% for Na₂SO₄) as well as enhanced anti-fouling properties (flux recovery ratio of more than 100% with bovine serum albumin-fouling and antibacterial efficiency of 93.7% against Escherichia coli). The performance of the prepared membranes was superior to that of most other modified TFC NF membranes previously reported in the literature. This work presents the application potential of capsaicin derivatives in water treatment and desalination processes.

Application of neural networks in membrane separation

Artificial neural networks (ANNs) as a powerful technique for solving complicated problems in membrane separation processes have been employed in a wide range of chemical engineering applications. ANNs can be used in the modeling of different processes more easily than other modeling methods. Besides that, the computing time in the design of a membrane separation plant is shorter compared to many mass transfer models. The membrane separation field requires an alternative model that can work alone or in parallel with theoretical or numerical types, which can be quicker and, many a time, much more reliable. They are helpful in cases when scientists do not thoroughly know the physical and chemical rules that govern systems. In ANN modeling, there is no requirement for a deep knowledge of the processes and mathematical equations that govern them. Neural networks are commonly used for the estimation of membrane performance characteristics such as the permeate flux and rejection over the entire range of the process variables, such as pressure, solute concentration, temperature, superficial flow velocity, etc. This review investigates the important aspects of ANNs such as methods of development and training, and modeling strategies in correlation with different types of applications [microfiltration (MF), ultrafiltration (UF), nanofiltration (NF), reverse osmosis (RO), electrodialysis (ED), etc.]. It also deals with particular types of ANNs that have been confirmed to be effective in practical applications and points out the advantages and disadvantages of using them. The combination of ANN with accurate model predictions and a mechanistic model with less accurate predictions that render physical and chemical laws can provide a thorough understanding of a process.