Probing the Water Stability Limits and Degradation Pathways of Metal–Organic Frameworks

A comprehensive model to describe the water stability of prototypical metal–organic frameworks (MOFs) is derived by combining different types of theoretical and experimental approaches. The results provide an insight into the early stages of water-triggered destabilization of MOFs and allow detailed pathways to be proposed for the degradation of different MOFs under aqueous conditions. The essential elements of the approach are computing the pK_a values of coordinated water molecules and geometry relaxations. Variable-temperature and pH infrared spectroscopy techniques are used to corroborate the main findings. The model developed herein helps to explain stability limits observed for several prototypical MOFs, including MOF-5, HKUST-1, UiO-66, and MIL-101-Cr, in aqueous solutions, and thus, provides an insight into the possible degradation pathways in acidic and basic environments. The formation of a metal hydroxide through the autoprotolysis of metal-coordinated water molecules and the strength of carboxylate–metal interactions are suggested to be two key players that govern stability in basic and acidic media, respectively. The methodology presented herein can effectively guide future efforts, which are especially significant for in silico screening, for developing novel MOFs with enhanced aqueous stability.     

A new approach toward modeling of mixed-gas sorption in glassy polymers based on metaheuristic algorithms

Modeling mixed-gas sorption has always been associated with computational challenges due to the existence of two or more conflicting objective functions. This study aims to use an artificial intelligence approach toward modeling mixed-gas sorption in PIM-1 and TZ-PIM polymeric membranes. Non-dominated sorting genetic algorithm (NSGA-II) has been applied to identify the extended Henry-Langmuir (EHL) isotherm based on CO₂-CH₄ mixed-gas sorption data. Also, the group method of data handling (GMDH) neural network is implemented to obtain a formula for the calculation of equilibrium partial pressure corresponding to three effective parameters, which are easily measurable. The formula provides an accurate estimation from the equilibrium relationship between the partial pressure of each gas in the binary gas mixtures over the PIM-1 and TZ-PIM membranes. Eventually, the calculated coefficients of EHL isotherm and obtained formula for computing the partial pressure of each component are simultaneously applied into the isotherm model to predict the mixed-gas sorption behavior. The results showed that the computed lines well reproduce the experimental data points, proving that the applied artificial intelligence approach offers a suitable approximation for mixed-gas sorption.     

Controlled elitist multi-objective genetic algorithm joined with neural network to study the effects of nano-clay percentage on cell size and polymer foams density of PVC/clay nanocomposites

The parameters of foaming and nano-clay percentage on the density of polymer foam and cell size with the PVC field is studied. Cell size and density have a significant impact on the strength of foam and its insulation (including sounds and thermal insulation). By optimizing cell size and density, foam can be produced with the best mechanical properties. In foaming process of the nanocomposite samples by mass method, the design variables (input parameters) are foaming time and temperature and MMT content. The controlled elitist multi-objective GA is applied to minimize both the foam density and the cell size. To that end, the population size and the Pareto fraction are selected as 100 and 0.5, respectively. The noninferior solution obtained by the controlled elitist multi-objective GA is illustrated. When both the MMT and the temperature are high, the resulting foam does not have ideal characteristics.

     

Click approach to the three‐component synthesis of novel β‐hydroxy‐1,2,3‐triazoles catalysed by new (Cu/Cu2O) nanostructure as a ligand‐free,green and regioselective nanocatalyst in water

Copper nanostructures were produced as an effective and regioselective catalyst for the synthesis of 1,2,3‐triazoles from a wide range of raw materials, such as sodium azide, epoxides and terminal alkynes, in water via a one‐pot three‐component click reaction. The new heterogeneous catalyst was prepared by a simple ball mill reduction of CuO with NaBH₄ using a ball‐to‐powder weight ratio of 50:1 under air atmosphere at room temperature. The catalyst was fully characterized using scanning electron microscopy, energy‐dispersive X‐ray analysis, Fourier transform infrared spectroscopy and X‐ray diffraction. The copper nanostructures catalysed both ring opening and triazole cyclization steps. Products were obtained in high yields and short reaction times. The reactions were performed at ambient temperature in water as a green solvent. The Cu/Cu₂O nanostructures revealed high reusability and high stability via a simple recycling process.     

Systematic microstructural development with thermal diffusivity behaviour from nanometric to micronic grains of strontium titanate

Journal of Thermal Analysis and Calorimetry - Strontium titanate is a promising candidate for applications in thermoelectric, thermal management applications, and modern electronic devices because...