Porous Electrospun Fibers with Self‐Sealing Functionality: An Enabling Strategy for Trapping Biomacromolecules

Stimuli‐responsive porous polymer materials have promising biomedical application due to their ability to trap and release biomacromolecules. In this work, a class of highly porous electrospun fibers is designed using polylactide as the polymer matrix and poly(ethylene oxide) as a porogen. Carbon nanotubes (CNTs) with different concentrations are further impregnated onto the fibers to achieve self‐sealing functionality induced by photothermal conversion upon light irradiation. The fibers with 0.4 mg mL^?1 of CNTs exhibit the optimum encapsulation efficiency of model biomacromolecules such as dextran, bovine serum albumin, and nucleic acids, although their photothermal conversion ability is slightly lower than the fibers with 0.8 mg mL^?1 of CNTs. Interestingly, reversible reopening of the surface pores is accomplished with the degradation of PLA, affording a further possibility for sustained release of biomacromolecules after encapsulation. Effects of CNT loading on fiber morphology, structure, thermal/mechanical properties, degradation, and cell viability are also investigated. This novel class of porous electrospun fibers with self‐sealing capability has great potential to serve as an enabling strategy for trapping/release of biomacromolecules with promising applications in, for example, preventing inflammatory diseases by scavenging cytokines from interstitial body fluids.     

Natural frequency and buckling optimization of laminated hybrid composite plates using genetic algorithm and simulated annealing

In this study, genetic algorithm and simulated annealing are used to maximize natural frequency and buckling loads of simply supported hybrid composite plates. The aim of the study is to use two different techniques of optimization on the frequency and buckling optimization of composite plates, and compare the techniques for their effectiveness. The composite plate is made of carbon/epoxy and glass/epoxy hybrid plies, and assumed to be symmetric and balanced. The effect of hybridization is investigated using both techniques. The buckling problem has many maxima in the vicinity of local maxima. The best configurations are identified for different plate aspect ratios. The performance of both techniques is compared in terms of number of function evaluations as well as the capability of finding best configurations.     

Design and Implementation of a Multi Sensor Based Brain Computer Interface for a Robotic Wheelchair

In this study, design and implementation of a multi sensor based brain computer interface for disabled and/or elderly people is proposed. Developed system consists of a wheelchair, a high-power motor controller card, a Kinect camera, electromyogram (EMG) and electroencephalogram (EEG) sensors and a computer. The Kinect sensor is installed on the system to provide safe navigation for the system. Depth frames, captured by the Kinect’s infra-red (IR) camera, are processed with a custom image processing algorithm in order to detect obstacles around the wheelchair. A Consumer grade EMG device (Thalmic Labs) was used to obtain eight channels of EMG data. Four different hand movements: Fist, release, waving hand left and right are used for EMG based control of the robotic wheelchair. EMG data is first classified using artificial neural network (ANN), support vector machines and random forest schemes. The class is then decided by a rule-based scheme constructed on the individual outputs of the three classifiers. EEG based control is adopted as an alternative controller for the developed robotic wheelchair. A wireless 14-channels EEG sensor (Emotiv Epoch) is used to acquire real time EEG data. Three different cognitive tasks: Relaxing, math problem solving, text reading are defined for the EEG based control of the system. Subjects were asked to accomplish the relative cognitive task in order to control the wheelchair. During experiments, all subjects were able to control the robotic wheelchair by hand movements and track a pre-determined route with a reasonable accuracy. The results for the EEG based control of the robotic wheelchair are promising though vary depending on user experience.     

Aqueous solubility and alkaline hydrolysis of the novel high explosive hexanitrohexaazaisowurtzitane (CL-20)

The recently developed polycyclic nitramine CL-20 is considered as a possible replacement for the monocyclic nitramines RDX and HMX. The present study reports aqueous solubility data for CL-20, as well as the kinetic parameters for its alkaline hydrolysis with sodium hydroxide below and above its solubility limits. Aqueous solubility of CL-20 was measured in the temperature range of 4–69 °C and the data were fitted to a generalized solubility model. Alkaline hydrolysis experiments were conducted at 15, 20, 30 and 40 °C, with hydroxide concentrations ranging from 0.25 to 300 mM. Like RDX and HMX, alkaline hydrolysis of CL-20 follows second-order kinetics. CL-20 alkaline hydrolysis was found to proceed at a significantly faster rate than RDX. The temperature dependency of the second-order rate constants was evaluated using the Arrhenius model. The activation energy for CL-20 was found to be within close range of the activation energies reported for RDX and HMX.     

Adsorption of phenol by MMT-CTAB and WPT-CTAB: Equilibrium,kinetic, and thermodynamic study

In this study, the phenol adsorption capacity of cetyltrimethylammonium bromide modified clays (MMT-CTAB) and cetyltrimethylammonium bromide modified pulp tea (WPT-CTAB) were studied. In batch adsorption experiments performed with MMT-CTAB, the effects of parameters such contact time, phenol concentration, pH of solution and adsorbent dosage were investigated. The effect of temperature on phenol adsorption onto MMT-CTAB and WPT-CTAB was examined; equilibrium and thermodynamic studies were completed. The highest phenol removal was found at pH 4.0 for MMT-CTAB and WPT-CTAB. To analyze the kinetics of phenol adsorption onto MMT-CTAB, the pseudo first-order and pseudo second-order kinetic models were applied. The kinetic data fitted better to the pseudo second-order model than the pseudo first-order kinetic model for MMT-CTAB. The characterization of adsorbents in phenol adsorption was clarified with the FTIR technique. Thermodynamic parameters such as ΔH°, ΔS° and ΔG° were calculated for each adsorption process. The adsorption process was found to be exothermic and spontaneous for phenol adsorption by MMT-CTAB and WPT-CTAB. The results were analyzed with the Langmuir, Freundlich, Temkin and Harkins–Jura equations using linearized correlation coefficients at different temperatures. The Langmuir equation was found to best represent the equilibrium data for phenol adsorption onto MMT-CTAB and WPT-CTAB.     

Breast cancer detection using K‐nearest neighbors data mining method obtained from the bow‐tie antenna dataset

Breast cancer, has been a significant cancer type for women on the society. Early diagnosis and timely medical treatment are important key factors spreading to the other tissues and permitting long‐time survival of patients. Since the existing methods have several serious shortfalls, microwave imaging method for the diagnosis of early stage tumors has been interested by different scientific research groups in terms of moderating endogenous the electrical property difference between healthy tissue and malignancies. In this article, both an ultra‐wideband bow‐tie antenna with enhanced bandwidth and a 3D breast model which has different electrical properties which are permittivity and conductivity is created in simulation tool to solve electromagnetic field values. Return loss, VSWR, and radiation pattern characteristics, which are significant antenna parameters, are simulated and obtained whether the antenna possess an efficient characteristic or not. Electric field values over the breast tissue in which there is a tumor or not tumor are evaluated. In this article, above‐mentioned values of frequency bandwidth, dielectric constant of antenna's substrate, electric field, and tumor information were consisted in their dataset. This dataset obtained from the Bow‐Tie Antenna was used to detect the breast cancer with one of the data mining method, which is K‐Nearest Neighbor Algorithm.