A study of carboxylic acids and nano-TiO2 effects on stress relaxation of cotton fabrics

This study presents an evaluation on stress relaxation of cotton fabrics before and after performing the finishing process. The process includes a series of treatments on fabric samples through the Pad-dry curing method. A group of organic acids along with a proportion of catalyst and co-catalyst mixture constituted the treatment agents. Stress relaxation behavior of untreated and treated samples was assessed along two different directions of weft and warp using stress relaxation tester. In addition, an Italian Mesdan tensile tester was applied to measure physical and mechanical properties such as elongation, strength, Poisson’s ratio, and shear modulus. In the mean time scanning electron microscopy was applied to consider surface of samples before and after treatments. The results prove that the stress relaxation of treated samples has shown reasonably acceptable values when compared with those obtained for untreated one. Furthermore, the curve fitting of Maxwell’s model over the experimental data also justified that an interlaced model is more appropriate for explaining the stress relaxation in cotton fabrics. Beside the result of stress relaxation, Poisson’s ratio, and shear modulus illustrated remarkable increments. On the other hand, a reverse trend was observed for tensile properties in both directions. Orthotropic feature evaluation of fabrics toward both the direction of warp and weft also infers different response of stress relaxation in each direction.     

Multi-scale modelling of elastic moduli of trabecular bone

We model trabecular bone as a nanocomposite material with hierarchical structure and predict its elastic properties at different structural scales. The analysis involves a bottom-up multi-scale approach, starting with nanoscale (mineralized collagen fibril) and moving up the scales to sub-microscale (single lamella), microscale (single trabecula) and mesoscale (trabecular bone) levels. Continuum micromechanics methods, composite materials laminate theory and finite-element methods are used in the analysis. Good agreement is found between theoretical and experimental results.     

Toughening of epoxy resin systems using core–shell rubber particles: a literature review

Journal of Materials Science - Excellent thermal and mechanical properties and high chemical resistance with low shrinkage of epoxy resins open a wide window of various industrial applications,...     

Biosynthesis of zinc oxide nanoparticles using anjbar (root of Persicaria bistorta) extract and their cytotoxic effects on human breast cancer cell line (MCF‐7)

Biosynthesis of nanoparticles (NPs) using biomass is now one of the best methods for synthesising NPs due to their nontoxic and biocompatibility. Plants are the best choice among all biomass to synthesise large‐scale NPs. The objectives of this study were to synthesise zinc oxide nanoparticles (ZnO‐NPs) using Anjbar (root of Persicaria bistorta) [An/ZnO‐NPs] and investigate the cytotoxic and anti‐oxidant effects. For this purpose, the An/ZnO‐NPs were synthesised by using Bistort extract and characterised using UV–Visible spectroscopy, transmission electron microscope, field emission scanning electron microscope, x‐ray diffraction and Fourier‐transform infrared spectroscopy. The cytotoxic effects of the An/ZnO‐NPs on MCF‐7 cells were followed by 3‐(4,5‐dimethylthiazol‐2‐yl)‐2,5‐diphenyltetrazolium bromide assays at 24, 48, and 72 h. Nuclear morphology changed and apoptosis in cells was investigated using acridine orange/propodium iodide (AO/PI) staining and flow cytometry analysis. The pure biosynthesised ZnO‐NPs were spherical in shape and particles sizes ranged from 1 to 50 nm. Treated MCF‐7 cells with different concentrations of ZnO‐NPs inhibited cell viability in a time‐ and dose‐dependent manner with IC50 about 32 μg/ml after 48 h of incubation. In flow cytometry analysis the sub‐G1 population, which indicated apoptotic cells, increased from 12.6% at 0 μg/ml (control) to 92.8% at 60 μg/ml, 48 h after exposure. AO/PI staining showed that the treated cells displayed morphologic evidence of apoptosis, compared to untreated groups. Inspec keywords: cancer, cellular biophysics, toxicology, particle size, nanofabrication, X‐ray diffraction, nanomedicine, nanoparticles, ultraviolet spectra, scanning electron microscopy, visible spectra, transmission electron microscopy, patient treatment, field emission electron microscopy, Fourier transform infrared spectra, drug delivery systemsOther keywords: anjbar, cytotoxic effects, human breast cancer cell line, biomass, transmission electron microscope, field emission scanning electron microscope, Fourier‐transform infrared spectroscopy, flow cytometry analysis, ZnO‐NPs inhibited cell viability, antioxidant effects, MCF‐7 cells, biosynthesised ZnO‐NP, biosynthesised ZnO‐NP, acridine orange‐propodium iodide staining, An‐ZnO‐NP, Persicaria bistorta, zinc oxide nanoparticle biosynthesis, 3‐(4,5‐dimethylthiazol‐2‐yl)‐2,5‐diphenyltetrazolium bromide     

Graphene Nanomesh Promises Extremely Efficient In Vivo Photothermal Therapy

Reduced graphene oxide nanomesh (rGONM), as one of the recent structures of graphene with a surprisingly strong near‐infrared (NIR) absorption, is used for achieving ultraefficient photothermal therapy. First, by using TiO₂ nanoparticles, graphene oxide nanoplatelets (GONPs) are transformed into GONMs through photocatalytic degradation. Then rGONMs functionalized by polyethylene glycol (PEG), arginine–glycine–aspartic acid (RGD)‐based peptide, and cyanine 7 (Cy7) are utilized for in vivo tumor targeting and fluorescence imaging of human glioblastoma U87MG tumors having α_νβ₃ integrin receptors, in mouse models. The rGONM‐PEG suspension (1 μg mL^?1) exhibits about 4.2‐ and 22.4‐fold higher NIR absorption at 808 nm than rGONP‐PEG and graphene oxide (GO) with lateral dimensions of ≈60 nm and ≈2 μm. In vivo fluorescence imaging demonstrates high selective tumor uptake of rGONM‐PEG‐Cy7‐RGD in mice bearing U87MG cells. The excellent NIR absorbance and tumor targeting of rGONM‐PEG‐Cy7‐RGD results in an ultraefficient photothermal therapy (100% tumor elimination 48 h after intravenous injection of an ultralow concentration (10 μg mL^?1) of rGONM‐PEG‐Cy7‐RGD followed by irradiation with an ultralow laser power (0.1 W cm^?2) for 7 min), whereas the corresponding rGO‐ and rGONP‐based composites do not present remarkable treatments under the same conditions. All the mice treated by rGONM‐PEG‐Cy7‐RGD survived over 100 days, whereas the mice treated by other usual rGO‐based composites were dead before 38 days. The results introduce rGONM as one of the most promising nanomaterials in developing highly desired ultraefficient photothermal therapy.     

Investigation on the Effect of Sulfur and Titanium on the Microstructure of Lamellar Graphite Iron

The goal of this work was to identify the inclusions in lamellar graphite cast iron in an effort to explain the nucleation of the phases of interest. Four samples of approximately the same carbon equivalent but different levels of sulfur and titanium were studied. The Ti/S ratios were from 0.15 to 29.2 and the Mn/S ratios from 4.2 to 48.3. Light and electron microscopy were used to examine the unetched, color-etched, and deep-etched samples. It was confirmed that in irons with high sulfur content (0.12 wt pct) nucleation of type-A and type-D graphite occurs on Mn sulfides that have a core of complex Al, Ca, Mg oxide. An increased titanium level of 0.35 pct produced superfine interdendritic graphite (~10 μm) at low (0.012 wt pct) as well as at high-S contents. Ti also caused increased segregation in the microstructure of the analyzed irons and larger eutectic grains (cells). TiC did not appear to be a nucleation site for the primary austenite as it was found mostly at the periphery of the secondary arms of the austenite, in the last region to solidify. The effect of titanium in refining the graphite and increasing the austenite fraction can be explained through the widening of the liquidus-eutectic temperature interval (more time for austenite growth) and the decrease in the growth rate of the graphite because of Ti absorption on the graphite. The fact that Ti addition produced larger eutectic cells supports the theory that Ti is not producing finer graphite because of a change in the nucleation potential, but because of lower growth rate of the graphite in between the dendrite arms of a larger fraction of austenite. In the presence of high-Ti and S, (MnTi)S star-like and rib-like inclusions precipitate and act as nuclei for the austenite.     

Concurrent wing and high-lift system aerostructural optimization

A method is presented for concurrent aerostructural optimization of wing planform, airfoil and high lift devices. The optimization is defined to minimize the aircraft fuel consumption for cruise, while satisfying the field performance requirements. A coupled adjoint aerostructural tool, that couples a quasi-three-dimensional aerodynamic analysis method with a finite beam element structural analysis is used for this optimization. The Pressure Difference Rule is implemented in the quasi-three-dimensional analysis and is coupled to the aerostructural analysis tool in order to compute the maximum lift coefficient of an elastic wing. The proposed method is able to compute the maximum wing lift coefficient with reasonable accuracy compared to high-fidelity CFD tools that require much higher computational cost. The coupled aerostructural system is solved using the Newton method. The sensitivities of the outputs of the developed tool with respect to the input variables are computed through combined use of the chain rule of differentiation, automatic differentiation and coupled-adjoint method. The results of a sequential optimization, where the wing shape and high lift device shape are optimized sequentially, is compared to the results of simultaneous wing and high lift device optimization.     

An Overview of Audio Event Detection Methods from Feature Extraction to Classification

Audio streams, such as news broadcasting, meeting rooms, and special video comprise sound from an extensive variety of sources. The detection of audio events including speech, coughing, gunshots, etc. leads to intelligent audio event detection (AED). With substantial attention geared to AED for various types of applications, such as security, speech recognition, speaker recognition, home care, and health monitoring, scientists are now more motivated to perform extensive research on AED. The deployment of AED is actually a more complicated task when going beyond exclusively highlighting audio events in terms of feature extraction and classification in order to select the best features with high detection accuracy. To date, a wide range of different detection systems based on intelligent techniques have been utilized to create machine learning-based audio event detection schemes. Nevertheless, the preview study does not encompass any state-of-the-art reviews of the proficiency and significances of such methods for resolving audio event detection matters. The major contribution of this work entails reviewing and categorizing existing AED schemes into preprocessing, feature extraction, and classification methods. The importance of the algorithms and methodologies and their proficiency and restriction are additionally analyzed in this study. This research is expanded by critically comparing audio detection methods and algorithms according to accuracy and false alarms using different types of datasets.     

A method of learning weighted similarity function to improve the performance of nearest neighbor

The performance of Nearest Neighbor (NN) classifier is known to be sensitive to the distance (or similarity) function used in classifying a test instance. Another major disadvantage of NN is that it uses all training instances in the generalization phase. This can cause slow execution speed and high storage requirement when dealing with large datasets. In the past research, many solutions have been proposed to handle one or both of the above problems. In the scheme proposed in this paper, we tackle both of these problems by assigning a weight to each training instance. The weight of a training instance is used in the generalization phase to calculate the distance (or similarity) of a query pattern to that instance. The basic NN classifier can be viewed as a special case of this scheme that treats all instances equally (by assigning equal weight to all training instances). Using this form of weighted similarity measure, we propose a learning algorithm that attempts to maximize the leave-one-out (LV1) classification rate of the NN rule by adjusting the weights of the training instances. At the same time, the algorithm reduces the size of the training set and can be viewed as a powerful instance reduction technique. An instance having zero weight is not used in the generalization phase and can be virtually removed from the training set. We show that our scheme has comparable or better performance than some recent methods proposed in the literature for the task of learning the distance function and/or prototype reduction.     

Mechanical and cellular characterization of electrospun poly(l-lactic acid)/gelatin yarns with potential as angiogenesis scaffolds

Iranian Polymer Journal - Electrospun PLLA/gelatin yarns with different compositions were fabricated as tissue engineering scaffolds. The yarns were characterized by scanning electron microscopy...