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.     

Lead recovery from aqueous environment by using porous carbon of citrus fruits waste: equilibrium,kinetics and thermodynamic studies

ABSTRACT

The mixture of citrus fruits (orange, grapefruit, mandarin, and lemon) wastes was utilized to obtain high surface area activated carbon (AC) by H₃PO₄ activation. The production conditions were optimized and the optimum conditions were determined. The optimal-activated carbon (CFWAC) was characterized by various physicochemical techniques. CFWAC was also used as a sorbent for Pb (II) ions from water. Batch experiments were performed to explore the adsorption capacity and mechanism. The Langmuir isotherm and pseudo-second-order kinetic model showed good fitness to the experimental data. The maximum Pb (II) adsorption capacity of CFWAC was found to be 163.93 mg/g.     

Improvements of the structural,thermal, and mechanical properties of structural adhesive with functionalized boron nitride nanoparticles

Investigations on the production and development of nanoparticle-reinforced polymer materials have been attracted attention by researchers. Various nanoparticles have been used to improve the mechanical, chemical, thermal, and physical properties of polymer matrix composites. Boron compounds come to the fore to improve the mechanical and thermal properties of polymers. In this study, mechanical, thermal, and structural properties of structural adhesive have been examined by adding nano hexagonal boron nitride (h-BN) to epoxy matrix at different percentages (0.5, 1, 2, 3, 4, and 5%). For this purpose, nano h-BN particles were functionalized with 3-aminopropyltriethoxysilane (APTES) to disperse the h-BN nanoparticles homogeneously in epoxy matrix and to form a strong bond at the matrix interface. Two-component structural epoxy adhesive was modified by using functionalized h-BN nanoparticles. The structural and thermal properties of the modified adhesives were investigated by scanning electron microscopy and energy dispersion X-ray spectroscopy, Fourier transform infrared spectroscopy, differential scanning calorimetry, and thermogravimetric analysis techniques. Tensile test and dynamic mechanical analysis were performed to determine the mechanical properties of the adhesives. When the results obtained from analysis were examined, it was seen that the nano h-BN particles functionalized with APTES were homogeneously dispersed in the epoxy matrix and formed a strong bond. In addition that, it was concluded from the experimental results that the thermal and mechanical properties of adhesives were improved by adding functionalized nano h-BN particles into epoxy at different ratios.     

Structural,thermal, and mechanical properties of silanized boron carbide doped epoxy nanocomposites

In the presented study, the structural, thermal, and mechanical properties of the nanocomposites were investigated by doping silanized hexagonal boron carbide (h-B₄C) nanoparticles in varying proportions (0.5%, 1%, 2%, 3%, 4%, and 5%) into the epoxy resin by weight. For this purpose, the surfaces of h-B₄C nanoparticles were silanized by using 3-(glycidyloxypropyl) trimethoxysilane (GPS) to improve adhesion between h-B₄C nanoparticles and epoxy matrix. Then, the silanized nanoparticles were added to the resin by ultrasonication and mechanical stirring techniques to produce nanocomposites. The bond structure differences of silanized B₄C nanoparticles (s-B₄C) and nanoparticle doped composites were investigated by using Fourier transform infrared spectroscopy. Scanning electron microscopy and energy dispersion X-ray spectroscopy (SEM-EDS) technique was used to examine the distribution of nanoparticles in the modified nanocomposites. Differential scanning calorimetry and thermogravimetric analysis techniques were used to determine the thermal properties of the neat and s-B₄C doped nanocomposites. The tensile test and dynamic mechanical analysis were performed to determine the mechanical properties. When the experimental results were examined, changes in the bonding structure of the s-B₄C nanoparticles doped nanocomposites and significant improvements in the mechanical and thermal properties were observed. The optimum doping ratio was determined as 2% by weight. At this doping ratio, the T_g, tensile strength and storage modulus increased approximately 18%, 35%, and 44% compared to the neat composite, respectively.     

Prediction of flank wear by using back propagation neural network modeling when cutting hardened H-13 steel with chamfered and honed CBN tools

Productivity and quality in the finish turning of hardened steels can be improved by utilizing predicted performance of the cutting tools. This paper combines predictive machining approach with neural network modeling of tool flank wear in order to estimate performance of chamfered and honed Cubic Boron Nitride (CBN) tools for a variety of cutting conditions. Experimental work has been performed in orthogonal cutting of hardened H-13 type tool steel using CBN tools. At the selected cutting conditions the forces have been measured using a piezoelectric dynamometer and data acquisition system. Simultaneously flank wear at the cutting edge has been monitored by using a tool makers microscope. The experimental force and wear data were utilized to train the developed simulation environment based on back propagation neural network modeling. A trained neural network system was used in predicting flank wear for various different cutting conditions. The developed prediction system was found to be capable of accurate tool wear classification for the range it had been trained.     

The influence of friction models on finite element simulations of machining

In the analysis of orthogonal cutting process using finite element (FE) simulations, predictions are greatly influenced by two major factors; a) flow stress characteristics of work material at cutting regimes and b) friction characteristics mainly at the tool-chip interface. The uncertainty of work material flow stress upon FE simulations may be low when there is a constitutive model for work material that is obtained empirically from high-strain rate and temperature deformation tests. However, the difficulty arises when one needs to implement accurate friction models for cutting simulations using a particular FE formulation. In this study, an updated Lagrangian finite element formulation is used to simulate continuous chip formation process in orthogonal cutting of low carbon free-cutting steel. Experimentally measured stress distributions on the tool rake face are utilized in developing several different friction models. The effects of tool-chip interfacial friction models on the FE simulations are investigated. The comparison results depict that the friction modeling at the tool-chip interface has significant influence on the FE simulations of machining. Specifically, variable friction models that are developed from the experimentally measured normal and frictional stresses at the tool rake face resulted in most favorable predictions. Predictions presented in this work also justify that the FE simulation technique used for orthogonal cutting process can be an accurate and viable analysis as long as flow stress behavior of the work material is valid at the machining regimes and the friction characteristics at the tool-chip interface is modeled properly.     

Design of a decision support system for machine tool selection based on machine characteristics and performance tests

Economic globalization, together with heightened market competition and increasingly short product life cycles are motivating companies to use advanced manufacturing technologies. Use of high speed machining is increasingly widespread; however, as the technology is relatively new, it lacks a deep-rooted knowledge base which would facilitate implementation. One of the most frequent problems facing companies wishing to adopt this technology is selecting the most appropriate machine tool for the product in question and own enterprise characteristics. This paper presents a decision support system for high speed milling machine tool selection based on machine characteristics and performance tests. Profile machining tests are designed and conducted in participating machining centers. The decision support system is based on product dimension accuracy, process parameters such as feed rate and interpolation scheme used by CNC and machine characteristics such as machine accuracy and cost. Experimental data for process error and cycle operation time are obtained from profile machining tests with different geometrical feature zones that are often used in manufacturing of discrete parts or die/moulds. All those input parameters have direct impact on productivity and manufacturing cost. Artificial neural network models are utilized for decision support system with reasonable prediction capability.     

Multi-objective process optimization for micro-end milling of Ti-6Al-4V titanium alloy

Micro-end milling is one of the promising methods for rapid fabrication of features with 3D complex shapes. However, controlling the micro-end milling process to obtain the desired results is much harder compared to that of macro-end milling due to the size effect and uncontrollable factors. The problem is much pronounced when workpiece material is a difficult-to-process material such as titanium-based alloys which are widely used as material of choice for aircraft structures, turbine blades, and medical implants. In order to find the optimal process parameters which minimize the surface roughness and burr formation, experiments were conducted and models obtained with statistically based methods utilized in multi-objective particle swarm optimization to identify optimum process parameters. The results show that the average surface roughness can be minimized while burr formation is reduced concurrently.     

Dissolution Behavior of Electrodeposited Ni–W Alloys

Nickel (Ni)–Tungsten (W) alloys were electrodeposited galvanostatically (at–10 mA cm^–2) on copper substrate with 3 different W contents under the controlled hydrodynamic conditions and then the anodic dissolution behaviors of the alloys were observed by potentiodynamic polarization and electrochemical quartz crystal microbalance (EQCM) techniques. While the structure of the electrodeposited Ni–W alloy with low W content (15.90% W) was crystalline, that of the alloy with high W content (50.80% W) was nano-crystalline according to X-ray diffraction patterns. The increase in the W content of the electrodeposited Ni–W alloy resulted decrease at pH 3 and increase at pH 7 and 12.5 in the anodic currents of the alloy. The pH dependent dissolutions caused electrodeposited alloy surface to have W—enrichment at pH 3 and Ni—enrichment at pH 7 and 12.5. These observations indicated that the selective dissolution of Ni or W was the main mechanism in the anodic dissolution of the electrodeposited Ni–W alloys. The EQCM experiments conducted at pH 7 supported the presence of the selective dissolution mechanism that the anodic dissolution potential of W was 0.42 V lower than that of Ni in the electrodeposited Ni–W alloys.     

Comparison of lactic acid bacteria diversity during the fermentation of Tarhana produced at home and on a commercial scale

Food Science and Biotechnology - In this study, lactic acid bacteria diversity during the fermentation of homemade and commercially prepared Tarhana, a traditional fermented cereal food from...