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.     

Electrical and optical properties of fluorine-doped CdO films deposited by ultrasonic spray pyrolysis

The CdO:F samples have been deposited onto microscope glass substrates at 250 °C by ultrasonic spray pyrolysis method. With the incorporation of fluorine into CdO, the direct optical transition has shifted towards the shorter wavelengths, and the transparency of the material has increased at a given wavelength above the fundamental absorption edge. The shift in the absorption edge is explained by means of the Moss–Burstein effect, which is also supported with the results of the current–voltage characteristics. Here, a correlation has been established between the band broadening and the increase in conductivity due to the increase in carrier density.     

APPLICATION OF RESPONSE SURFACE METHODOLOGY FOR THE MAXIMIZATION OF CONCORA CRUSH RESISTANCE OF PAPERBOARD

Response surface methodology is often used by researchers in different fields to determine the optimum values for controlled variables to maximize or minimize the response variables. Either maximization or minimization might be necessary depending on the response property. For example, if the response variable represents the yield of a process, maximization could be necessary; on the other hand, if the response variable is the biological oxygen demand (BOD) of an effluent the aim would definitely be minimization

Response surface methodology can be used two ways. It can be applied to the full-scale production or it can be scaled to a laboratory or the pilot plant. When applied to the full-scale production, the method is known as evolutionary operation (EVOP). EVOP is the continuous optimization of a process. The optimum conditions in a production plant can change depending on many factors such as raw material, ambient temperature, and equipment wear. Therefore, controlled variables should be optimized continuously to keep the response variable as close as possible to the maximum or minimum value. Hence, controlled variables are systematically changed around a center point to depict any shift of the response variable from the extreme. A thorough discussion of EVOP is given by Box, Evolutionary Operation: A Method for Increasing Industrial Productivity, Appl. Statist., 6, 81-101 (1957).     

Degradation of 4-chloro-2-methylphenol in aqueous solution by electro-Fenton and photoelectro-Fenton processes

Degradation of 4-chloro-2-methylphenol (PCOC), a refractory toxic chemical emitted to the environment from the industrial production of phenoxy herbicides was studied in aqueous solution. Electro-Fenton and photoelectro-Fenton processes were used as the degradation methods. H₂O₂, produced by the reduction of oxygen at carbon cathode reacted with dissolved metal ions to form hydroxyl radicals, which in turn reacted with PCOC sequentially to degrade the aromatic ring. The effects of using different [Fe²⁺]/[PCOC]₀ and the effect of replacing Fe²⁺ by Mn²⁺ ion have been examined. It was found that degradation rate was increased with increasing [Fe²⁺]/[PCOC]₀ ratio from 2 to 4. However, the total charge utilized during the treatment was also increased. The efficiency of PCOC degradation was observed to be higher when Mn²⁺ was used as the catalyst. The mineralization of aqueous solutions of PCOC, withdrawn from the reactor at certain time interval, has been followed by total organic carbon (TOC) decay and dechlorination. A fast and complete degradation of the aromatic ring was achieved in photoelectro-Fenton system. 41.7% TOC decay and complete dechlorination were observed by consuming only 141.4 C electrical charge during a 300 min photoelectron-Fenton treatment. In the case of electro-Fenton system, 280.7 C electrical charge was consumed during 450 min of electrolysis to attain a similar degradation of PCOC. 14.9% TOC removal and 89.3% dechlorination have been obtained in this system under the applied conditions.     

Predicting nepheline precipitation in waste glasses using ternary submixture model and machine learning

Nepheline precipitation in nuclear waste glasses during vitrification can be detrimental due to the negative effect on chemical durability often associated with its formation. Developing models to accurately predict nepheline precipitation from compositions is important for increasing waste loading since existing models can be overly conservative. In this study, an expanded dataset of 955 glasses, including 352 high-level waste glasses, was compiled from literature data. Previously developed submixture models were refitted using the new dataset, where a misclassification rate of 7.8% was achieved. In addition, nine machine learning (ML) algorithms (k-nearest neighbor, Gaussian process regression, artificial neural network, support vector machine, decision tree, etc.) were applied to evaluate their ability to predict nepheline precipitation from glass compositions. Model accuracy, precision, recall/sensitivity, and F1 scores were systemically compared between different ML algorithms and modeling protocols. Model prediction with an accuracy of ~0.9 (misclassification rate of ~10%) was observed for different algorithms under certain protocols. This study evaluated various ML models to predict nepheline precipitation in waste glasses, highlighting the importance of data preparation and modeling protocol, and their effect on model stability and reproducibility. The results provide insights into applying ML to predict glass properties and suggest areas for future research on modeling nepheline precipitation.     

Edge cracks in a transversely isotropic hollow cylinder

The analytical solution for the linear elastic, axisymmetric problem of inner and outer edge cracks in a transversely isotropic infinitely long hollow cylinder is considered. The z = 0 plane on which the crack lies is a plane of symmetry. The loading is uniform crack surface pressure. The mixed boundary value problem is reduced to a singular integral equation where the unknown is the derivative of the crack surface displacement. An asymptotic analysis is done to derive the generalized Cauchy kernel associated with edge cracks. It is shown that the stress intensity factor is a function of three material parameters. The singular integral equation is solved numerically. Stress intensity factors are presented for various values of material and geometric parameters.     

Carboxymethyl sago pulp/chitosan hydrogel as an immobilization medium for activated sludge for p-nitrophenol biodegradation

In this study, carboxymethyl sago pulp (CMSP) derived from sago waste was successfully crosslinked with ferric ions in the presence of chitosan forming a novel immobilization matrix for p-nitrophenol (PNP)-acclimated activated sludge. On the basis of the shortest biodegradation time of PNP, the optimized operational conditions of immobilization were found to be: 7 w/v% CMSP, 9 g L⁻¹ of activated sludge, 0.1 M ferric ion, and 15 min of crosslinking time. Observable inhibited PNP biodegradation was exhibited by the suspended activated sludge at the initial PNP concentration of 400 mg L⁻¹. In contrast, complete mineralization was achieved by the CMSP/chitosan-immobilized activated sludge (CMSP/Ch-AS) beads. The results revealed the important role of CMSP/Ch hydrogel in protecting activated sludge from the toxicity of PNP. The CMSP/Ch-AS beads could be reused consecutively up to three and two cycles, respectively, for the biodegradation of PNP at 200 and 400 mg L⁻¹, with the attainment of more than 99% of PNP removal at each cycle. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2019 , 136, 47531.     

Stochastic resonance in binary composite hypothesis-testing problems in the Neyman–Pearson framework

Performance of some suboptimal detectors can be enhanced by adding independent noise to their inputs via the stochastic resonance (SR) effect. In this paper, the effects of SR are studied for binary composite hypothesis-testing problems. A Neyman–Pearson framework is considered, and the maximization of detection performance under a constraint on the maximum probability of false-alarm is studied. The detection performance is quantified in terms of the sum, the minimum, and the maximum of the detection probabilities corresponding to possible parameter values under the alternative hypothesis. Sufficient conditions under which detection performance can or cannot be improved are derived for each case. Also, statistical characterization of optimal additive noise is provided, and the resulting false-alarm probabilities and bounds on detection performance are investigated. In addition, optimization theoretic approaches to obtaining the probability distribution of optimal additive noise are discussed. Finally, a detection example is presented to investigate the theoretical results.