3D-Printed MOF Monoliths:Fabrication Strategies and Environmental Applications

Metal-organic frameworks(MOFs)have been extensively considered as one of the most promising types of porous and crystalline organic-inorganic materials,thanks to their large specific surface area,high porosity,tailorable structures and compositions,diverse functionalities,and well-controlled pore/size distribution.However,most developed MOFs are in powder forms,which still have some technical challenges,including abrasion,dustiness,low packing densities,clogging,mass/heat transfer limitation,environmental pollution,and mechanical instability during the packing process,that restrict their applicability in industrial applications.Therefore,in recent years,attention has focused on techniques to convert MOF powders into macroscopic materials like beads,membranes,monoliths,gel/sponges,and nanofibers to overcome these challenges.Three-dimensional(3D)printing technology has achieved much interest because it can produce many high-resolution macroscopic frameworks with complex shapes and geometries from digital models.Therefore,this review summarizes the combination of different 3D printing strategies with MOFs and MOF-based materials for fabricating 3D-printed MOF monoliths and their environmental applications,emphasizing water treatment and gas adsorption/separation applications.Herein,the various strategies for the fabrication of 3D-printed MOF monoliths,such as direct ink writing,seed-assisted in-situ growth,coordination replication from solid precursors,matrix incorporation,selective laser sintering,and digital light processing,are described with the relevant examples.Finally,future directions and challenges of 3D-printed MOF monoliths are also presented to better plan future trajectories in the shaping of MOF materials with improved control over the structure,composition,and textural properties of 3D-printed MOF monoliths.

Development of a least squares support vector machine model for prediction of natural gas hydrate formation temperature

Hydrates always are considered as a threat to petroleum industry due to the operational problems it can cause.These problems could result in reducing production performance or even production stoppage for a long time.In this paper, we were intended to develop a LSSVM algorithm for prognosticating hydrate formation temperature(HFT) in a wide range of natural gas mixtures. A total number of 279 experimental data points were extracted from open literature to develop the LSSVM. The input parameters were chosen based on the hydrate structure that each gas species form. The modeling resulted in a robust algorithm with the squared correlation coefficients(R^2) of 0.9918. Aside from the excellent statistical parameters of the model, comparing proposed LSSVM with some of conventional correlations showed its supremacy, particularly in the case of sour gases with high H_2S concentrations, where the model surpasses all correlations and existing thermodynamic models. For detection of the probable doubtful experimental data, and applicability of the model, the Leverage statistical approach was performed on the data sets. This algorithm showed that the proposed LSSVM model is statistically valid for HFT prediction and almost all the data points are in the applicability domain of the model.

Polyurethane-SAPO-34 mixed matrix membrane for CO_2/CH_4 and CO_2/N_2 separation

SAPO-34 nanocrystals(inorganic filler) were incorporated in polyurethane membranes and the permeation properties of CO_2, CH_4,and N_2 gases were explored. In this regard, the synthesized PU-SAPO-34 mixed matrix membranes(MMMs) were characterized via SEM, AFM, TGA, XRD and FTIR analyses. Gas permeation properties of PU-SAPO-34 MMMs with SAPO-34 contents of 5 wt%, 10 wt% and 20 wt% were investigated. The permeation results revealed that the presence of 20 wt% SAPO-34 resulted in 4.45%, 18.24% and 40.2% reductions in permeability of CO_2,CH_4,and N_2, respectively, as compared to the permeability of neat polyurethane membrane. Also,the findings showed that at the pressure of 1.2 MPa, the incorporation of 20 wt% SAPO-34 into the polyurethane membranes enhanced the selectivity of CO_2/CH_4 and CO_2/N_2, 14.43 and 37.46%, respectively. In this research, PU containing 20 wt% SAPO-34 showed the best separation performance. For the first time, polynomial regression(PR) as a simple yet accurate tool yielded a mathematical equation for the prediction of permeabilities with high accuracy(R^2>99%).

A robust predictive tool for estimating CO2 solubility in potassium based amino acid salt solutions

The acid gas absorption in four potassium based amino acid salt solutions was predicted using artificial neural network(ANN). Two hundred fifty-five experimental data points for CO_2 absorption in the four potassium based amino acid salt solutions containing potassium lysinate, potassium prolinate, potassium glycinate, and potassium taurate were used in this modeling. Amine salt solution's type, temperature, equilibrium partial pressure of acid gas, the molar concentration of the solution, molecular weight, and the boiling point were considered as inputs to ANN to prognosticate the capacity of amino acid salt solution to absorb acid gas. Regression analysis was employed to assess the performance of the network. Levenberg–Marquardt back-propagation algorithm was used to train the optimal ANN with 5:12:1 architecture. The model findings indicated that the proposed ANN has the capability to predict precisely the absorption of acid gases in various amino acid salt solutions with Mean Square Error(MSE) value of 0.0011, the Average Absolute Relative Deviation(AARD) percent of 5.54%,and the correlation coefficient(R^2) of 0.9828.

A review on hollow fiber membrane module towards high separation efficiency:Process modeling in fouling perspective

Hollow fiber microfiltration(MF)and ultrafiltration(UF)membrane processes have been extensively used in water purification and biotechnology.However,complicated filtration hydrodynamics wield a negative influence on fouling mitigation and stability of hollow fiber MF/UF membrane processes.Thus,establishing a mathematical model to understand the membrane processes is essential to guide the optimization of module configurations and to alleviate membrane fouling.Here,we present a comprehensive overview of the hollow fiber MF/UF membrane filtration models developed from different theories.The existing models primarily focus on membrane fouling but rarely on the interactions between the membrane fouling and local filtration hydrodynamics.Therefore,more simplified conceptual models and integrated reduced models need to be built to represent the real filtration behaviors of hollow fiber membranes.Future analyses considering practical requirements including complicated local hydrodynamics and nonuniform membrane properties are suggested to meet the accurate prediction of membrane filtration performance in practical application.This review will inspire the development of high-efficiency hollow fiber membrane modules.