No Arabic abstract
Fouling is a major obstacle and challenge in membrane-based separation processes. Caused by the sophisticated interactions between foulant and membrane surface, fouling strongly depends on membrane surface chemistry and morphology. Current studies in the field have been largely focused on polymer membranes. Herein, we report a molecular simulation study for fouling on alumina and graphene membrane surfaces during water treatment. For two foulants (sucralose and bisphenol A), the fouling on alumina surfaces is reduced with increasing surface roughness; however, the fouling on graphene surfaces is enhanced by roughness. It is unravelled that the foulant-surface interaction becomes weaker in the ridge region of a rough alumina surface, thus allowing foulant to leave the surface and reducing fouling. Such behavior is not observed on a rough graphene surface because of the strong foulant-graphene interaction. Moreover, with increasing roughness, the hydrogen bonds formed between water and alumina surfaces are found to increase in number as well as stability. By scaling the atomic charges of alumina, fouling behavior on alumina surfaces is shifted to the one on graphene surfaces. This simulation study reveals that surface chemistry and roughness play a crucial role in membrane fouling, and the microscopic insights are useful for the design of new membranes towards high-performance water treatment.
We consider the main transition in single-component membranes using computer simulations of the Pink model [D. Pink {it et al.}, Biochemistry {bf 19}, 349 (1980)]. We first show that the accepted parameters of the Pink model yield a main transition temperature that is systematically below experimental values. This resolves an issue that was first pointed out by Corvera and co-workers [Phys. Rev. E {bf 47}, 696 (1993)]. In order to yield the correct transition temperature, the strength of the van der Waals coupling in the Pink model must be increased; by using finite-size scaling, a set of optimal values is proposed. We also provide finite-size scaling evidence that the Pink model belongs to the universality class of the two-dimensional Ising model. This finding holds irrespective of the number of conformational states. Finally, we address the main transition in the presence of quenched disorder, which may arise in situations where the membrane is deposited on a rough support. In this case, we observe a stable multi-domain structure of gel and fluid domains, and the absence of a sharp transition in the thermodynamic limit.
We have investigated the dynamics of water confined in mesostructured porous silicas (SBA-15, MCM-41) and four periodic mesoporous organosilicas (PMOs) by dielectric relaxation spectroscopy. The influence of water-surface interaction has been controlled by the carefully designed surface chemistry of PMOs that involved organic bridges connecting silica moieties with different repetition lengths, hydrophilicity and H-bonding capability. Relaxation processes attributed to the rotational motions of non-freezable water located in the vicinity of the pore surface were studied in the temperature range from 140 K to 225 K. Two distinct situations were achieved depending on the hydration level: at low relative humidity (33% RH), water formed a non-freezable layer adsorbed on the pore surface. At 75% RH, water formed an interfacial liquid layer sandwiched between the pore surface and the ice crystallized in the pore center. In the two cases, the study revealed different water dynamics and different dependence on the surface chemistry. We infer that these findings illustrate the respective importance of water-water and water-surface interactions in determining the dynamics of the interfacial liquid-like water and the adsorbed water molecules, as well as the nature of the different H-bonding sites present on the pore surface.
Recently, in an ensemble of small spheres, we proposed a method that converts the force between two large spheres into the pressure on the large spheres surface element. Using it, the density distribution of the small spheres around the large sphere can be obtained experimentally. In a similar manner, in this letter, we propose a transform theory for surface force apparatus, which transforms the force acting on the cylinder into the density distribution of the small spheres on the cylindrical surface. The transform theory we derived is briefly explained in this letter.
Recently, we proposed a method that converts the force between two-large colloids into the pressure on the surface element (FPSE conversion) in a system of a colloidal solution. Using it, the density distribution of the small colloids around the large colloid is calculated. In a similar manner, in this letter, we propose a transform theory for colloidal probe atomic force microscopy (colloidal probe AFM), which transforms the force acting on the colloidal probe into the density distribution of the small colloids on a flat surface. If measured condition is proper one, in our view, it is possible for the transform theory to be applied for liquid AFM and obtain the liquid structure. The transform theory we derived is briefly explained in this letter.
Machine learning models are poised to make a transformative impact on chemical sciences by dramatically accelerating computational algorithms and amplifying insights available from computational chemistry methods. However, achieving this requires a confluence and coaction of expertise in computer science and physical sciences. This review is written for new and experienced researchers working at the intersection of both fields. We first provide concise tutorials of computational chemistry and machine learning methods, showing how insights involving both can be achieved. We then follow with a critical review of noteworthy applications that demonstrate how computational chemistry and machine learning can be used together to provide insightful (and useful) predictions in molecular and materials modeling, retrosyntheses, catalysis, and drug design.