Flexible poly(styrene-ethylene-butadiene-styrene) hybrid nanofibers for bioengineering and water filtration applications

Among the thermoplastic elastomers that play important roles in the polymer industry due to their superior properties, styrene-based species and polyurethane block copolymers are of great interest. Poly(styrene-ethylene-butadiene-styrene) (SEBS) as a triblock copolymer seems to have the potential to meet many demands in different applications due to various industrial requirements where durability, biocompatibility, breaking elongation, and interfacial adhesion are important. In this study, the SEBS triblock copolymer was functionalized with natural (Satureja hortensis, SH) and synthetic (nanopowder, TiO₂) agents to obtain composite nanofibers by electrospinning and electrospraying methods for use in biomedical and water filtration applications. The results were compared with thermoplastic polyurethane (TPU) composite nanofibers, which are commonly used in these fields. Here, functionalized SEBS nanofibers exhibited antibacterial effect while at the same time improving cell viability. In addition, because of successful water filtration by using the SEBS composite nanofibers, the material may have a good potential to be used comparably to TPU for the application.     

Simultaneous multi-objective optimization of a new promoted ethylene dimerization catalyst using grey relational analysis and entropy measurement

A hybrid approach between the Taguchi method and grey relational analysis (GRA) with entropy measurement was applied to determine a single optimum setting for reaction factors of the proposed ethylene dimerization catalyst having overall selectivity to 1-butene (S_1-btn (%)) and turnover frequency (TOF (h^-1)) as multiple quality characteristics. Titanium tetrabutoxide (Ti(OC₄H₉)₄) catalyst precursor in combination with triethyl aluminum (TEA) activator, 1,4-dioxane as a suitable modifier, and ethylene dichloride (EDC) as a novel promoter were used in the catalysis. Control factors of temperature, pressure, Al/Ti, 1,4-dioxane/Ti, and EDC/Ti mol ratios were investigated on three levels and their main effects were discussed. The effect of the binary interaction between temperature, pressure, and Al/Ti mol ratio was also examined. Weight of the responses was determined using entropy. Analysis of variance (ANOVA) for data obtained from GRA indicated that EDC/Ti mol ratio with 27.64% contribution had the most profound effect on the multiple quality characteristics. Development of the weighted Grey-Taguchi method used the Taguchi method as its basic structure, adopted GRA to deal with multiple responses, and entropy to enhance the reasonability of the comprehensive index produced by GRA to make the results more objective and accurate. Overall, these combined mathematical techniques improved catalytic performance for 1-butene production.     

Modeling the oxidative coupling of methane using artificial neural network and optimizing of its operational conditions using genetic algorithm

The effect of some operating conditions such as temperature, gas hourly space velocity (GHSV), CH₄/O₂ ratio and diluents gas (mol% N₂) on ethylene production by oxidative coupling of methane (OCM) in a fixed bed reactor at atmospheric pressure was studied over Mn/Na₂WO₄/SiO₂ catalyst. Based on the properties of neural networks, an artificial neural network was used for model developing from experimental data. To prevent network complexity and effective data input to the network, principal component analysis method was used and the number of output parameters was reduced from 4 to 2. A feed-forward back-propagation network was used for simulating the relations between process operating conditions and those aspects of catalytic performance including conversion of methane, C₂ products selectivity, C2 yielding and C₂H₄/C₂H₆ ratio. Levenberg-Marquardt method is presented to train the network. For the first output, an optimum network with 4-9-1 topology and for the second output, an optimum network with 4-6-1 topology was prepared. After simulating the process as well as using ANNs, the operating conditions were optimized and a genetic algorithm based on maximum yield of C₂ was used. The average error in comparing the experimental and simulated values for methane conversion, C₂ products selectivity, yield of C₂ and C₂H₄/C₂H₆ ratio, was estimated as 2.73%, 10.66%, 5.48% and 10.28%, respectively.     

Kinetic Model for Invertase‐Induced Sucrose Hydrolysis: Initial Time Lag

The kinetics of sucrose hydrolysis by invertase was studied in order to find a comprehensive model for the reaction pathway and mechanism. First, three common models of Michaelis‐Menten (MM), substrate inhibition (S²), and substrate clusters' inhibition (S³(I)) were investigated. The third model was found to better predict the initial sucrose concentration. Then, the S³(I) model was modified to cover the remaining pathway (S³(II)). Finally, a new comprehensive model (S³(III)) was evaluated, which in addition to what is considered in the two previously mentioned models (S³(I) and S³(II)) also involved the initial time lag. The model predictions showed an excellent agreement with the experimental data. The mean absolute error for the MM model is significantly reduced for the S³(III) model.     

Performance of advanced color difference and CAM02‐based formulas in prediction of the Crispening effect for reflective samples

The Crispening effect is defined as an increase in the perceived color difference of the two stimuli, when their color (chromaticity or luminance) is close to the background on which the two stimuli are compared. In this study, the amount of the Crispening effect for three achromatic backgrounds and also the performance of six different color difference formulas (CDFs) for prediction of this effect have been investigated, by preparing 85 sample pairs in 9 CIE's recommended color centers. Regarding the results, the maximum (50%) and the minimum (4%) amount of the Crispening effect belong to the gray and the purple centers, respectively. According to the results of a comparative test, the Crispening intensifies when two stimuli have just lightness difference instead of just chromaticity difference. The highest variation was for the gray samples, in which the amount of the Crispening effect increased from 35% to 65%. By using PF/3 and STRESS index, it is also concluded that CMC and CIEDE2000 perform better than CAM02‐SCD and CAM02‐UCS in prediction of the Crispening effect on the dark gray and gray backgrounds. According to the results, the significant differences between the performances of the CDFs disappear when the luminance of the background increases. Huang's power functions also do not improve these results significantly. Furthermore, the results indicate that the traditional L* equation used in CIELAB performs similar to the Whittle's formula in prediction of the Crispening effect for reflective samples, and no significant difference was obtained.     

Fabricating and robust artificial neural network modeling nanoscale polyurethane fiber using electrospinning method

With regard to the fact that currently there is no comprehensive method to predict diameter of polyurethane/solvent fiber from electrospinning, in this study, diameter prediction of polyurethane/solvent fiber was conducted using neural networks and an error of 166 nm was observed. This error shows that artificial neural networks (ANNs) can predict diameter of electrospinning polyurethane fibers well. Then, considering weak repeatability nature of electrospinning in fabricating fibers with desired diameter, least mean square is used to improve stability of neural network model that shows an error of 113 nm, which represented better results compared to common ANN. To investigate the effect of each one of parameters affecting fiber diameter, sensitivity analysis was conducted. Along with this predicting model, sensitivity analysis can be used to reduce parameters space before conducting future studies. © 2017 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2017 , 134, 45116.     

Influence of nanoclay on urea–glyoxalated lignin–formaldehyde resins for wood adhesive

In this research, the influence of nanoclay on urea–glyoxalated lignin–formaldehyde (GLUF) resin properties has been investigated. To prepare the GLUF resin, glyoxalated soda baggase lignin (15 wt%) was added as an alternative for the second urea during the UF resin synthesis. The prepared GLUF resin was mixed with the 0.5%, 1%, and 1.5% nanoclay by mechanically stirring for 5 min at room temperature. The physicochemical properties of the prepared resins were measured according to standard methods. Then the resins were used in particleboard production and the physical and mechanical properties of the manufactured panels were determined. Finally, from the results obtained, the best prepared resin was selected and its properties were analyzed by differential scanning calorimetry (DSC), Fourier transform infrared spectrometry (FTIR), and X-ray diffractometry (XRD). Generally the results indicated that the addition of sodium-montmorillonite (NaMMT) up to 1.5% appears to improve the performance of GLUF resins in particleboards. The results also showed that nanoclays improved mechanical strength (modulus of elasticity (MOE), Modulus of Rupture (MOR), and internal bond (IB) strength) of the panels bonded with GLUF resins. The panels containing GLUF resin and nanoclay yielded lower formaldehyde emission as well as water absorption content than those made from the neat GLUF resins. XRD characterization indicated that NaMMT only intercalated when mixed with GLUF resin. Based on DSC results, the addition of NaMMT could accelerate the curing of GLUF resins. The enthalpy of the cure reaction (ΔH) of GLUF resin containing NaMMT was increased compared with neat GLUF resin. Also the results of FTIR analysis indicated that addition of NaMMT change the GLUF resins structures.     

The effect of soda bagasse lignin modified by ionic liquids on properties of the urea–formaldehyde resin as a wood adhesive

The aim of this research was to investigate the influence of lignin modified by ionic liquids on physical and mechanical properties of plywood panels bonded with the urea–formaldehyde (UF) resin. For this purpose, soda bagasse lignin was modified by the 1-ethyl-3-methylimidazolium acetate ([Emim][OAc]) ionic liquid and then the various contents of unmodified and modified lignins (10, 15, and 20%) were added at pH=7 instead of second urea during the UF resin synthesis. The physicochemical properties of the prepared resins as well as the water absorption, shear strength, and formaldehyde emission of the plywood panels made with these adhesives were measured according to standard methods. According to Fourier Transform Infrared (FTIR) Spectrometry, by treatment of lignin, the C=O, C–C, and C–H bonds decrease while the content of the C–N bond dramatically increases. Based on the finding of this research, the performance of soda bagasse lignin in UF resins dramatically improves by modification by ILs; as the resins with modified lignin yielded lower formaldehyde emission and water absorption when compared to those made from unmodified lignin and commercial UF adhesives, respectively. The shear strength as well as wood failure percentages are lower for the panels produced with modified lignin than for the panels produced with UF resins alone.     

Hydrothermal synthesis of Co3O4 nanosheets and its application in photoelectrochemical water splitting

In this study, Co₃O₄ nanosheets were synthesized through hydrothermal method using cobalt nitrate hexahydrate. X-ray diffraction, diffuse reflectance spectra, Fourier transform infrared spectroscopy, energy-dispersive X-ray spectroscopy, and field emission scanning electron microscopy were applied to investigate the properties of as-synthesized samples. Ultimately, the electrochemical and photoelectrochemical properties were evaluated by Mott–Schottky analysis and measuring photoconversion efficiency of Co₃O₄ nanosheets. The results indicated that Co₃O₄ nanosheets exhibited a maximum efficiency of 0.92% for water electrolysis under simulated 1.5 global sunlight air mass, which further suggests the excellent potential of Co₃O₄ nanosheets for application in hydrogen generation through photocatalytic water splitting.     

Thermal decomposition kinetics of dynamically vulcanized polyamide 6–acrylonitrile butadiene rubber–halloysite nanotube nanocomposites

Thermally stable thermoplastic elastomer nanocomposites based on polyamide 6 (PA6), acrylonitrile butadiene rubber (NBR), and halloysite nanotubes (HNTs) were dynamically vulcanized, and their nonisothermal decomposition kinetics were examined. The Friedman, Kissinger–Akahira–Sunose (KAS), Ozawa–Wall–Flynn (FWO), and modified Coats–Redfern (m-CR) isoconversional models were used to obtain information about the kinetics of the thermal decomposition of PA6–NBR–HNTs in terms of the activation energy per partial mass loss monitored through thermogravimetric analyses performed at different heating rates. An erratic trend was due to the Friedman model, especially for systems having higher HNT loadings, whereas the KAS, FWO, and m-CR models revealed very similar meaningful thermal decomposition kinetics. A relatively high activation energy corroborating a reliable thermal stability was obtained by the addition of HNTs to PA6–NBR, and the resistance against decomposition was higher for systems containing more HNT. This signified the role of the HNTs as thermal stability modifiers. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2019 , 136, 47483.