In the current study, the effect of applied load, sliding speed, and type and weight percentages of reinforcements on the wear properties of ultrahigh molecular weight polyethylene (UHMWPE) was theoretically studied. The extensive experimental results were taken from literature and modeled with artificial neural network (ANN). The feed forward (FF) back-propagation (BP) neural network (NN) was used to predict the dry sliding wear behavior of UHMWPE composites. Eleven input vectors were used in the construction of the proposed NN. The carbon nanotube (CNT), carbon fiber (CF), graphene oxide (GO), and wollastonite additives are the main input parameters and the volume loss is the output parameter for the developed NN. It was observed that the sliding speed and applied load have a stronger effect on the volume loss of UHMWPE composites in comparison to other input parameters. The proper condition for achieving the desired wear behaviors of UHMWPE by tailoring the weight percentage and reinforcement particle size and composition was presented. The proposed NN model and the derived explicit form of mathematical formulation show good agreement with test results and can be used to predict the volume loss of UHMWPE composites.

In order to investigate the structure of welds, austenitic stainless steel (SS) studs with a diameter of 6 mm were welded to austenitic SS plates with a thickness of 5 mm using an arc stud welding (ASW) method. The effects of the welding current, welding time, and tip volume of the stud on the microstructure and ultimate tensile strength (UTS) of the welded samples were investigated in detail. The formation of δ-ferrites was detected in the weld zone because of the higher heat generated during the welding process. Higher welding current and time adversely affected the stud and significantly reduced the UTS of the samples. The UTS of the joints was also estimated using artificial neural network (ANN) and Taguchi approaches. The mathematical formulations for these two approaches were given in explicit form. Experimental results showed that the neural network results are more consistent with experimental results than those of the Taguchi method. Overall, it can be concluded that in order to achieve good welding joints and high strength values, ASW parameters should be investigated properly to determine the optimum conditions for each metal.

Hot electron light emission and lasing in semiconductor heterostructure (Hellish) devices are surface emitters the operation of which is based on the longitudinal injection of electrons and holes in the active region. These devices can be designed to be used as vertical cavity surface emitting laser or, as in this study, as a vertical cavity semiconductor optical amplifier (VCSOA). This study investigates the prospects for a Hellish VCSOA based on GaInNAs/GaAs material for operation in the 1.3-μm wavelength range. Hellish VCSOAs have increased functionality, and use undoped distributed Bragg reflectors; and this coupled with direct injection into the active region is expected to yield improvements in the gain and bandwidth. The design of the Hellish VCSOA is based on the transfer matrix method and the optical field distribution within the structure, where the determination of the position of quantum wells is crucial. A full assessment of Hellish VCSOAs has been performed in a device with eleven layers of Ga_0.35In_0.65N_0.02As_0.08/GaAs quantum wells (QWs) in the active region. It was characterised through I-V, L-V and by spectral photoluminescence, electroluminescence and electro-photoluminescence as a function of temperature and applied bias. Cavity resonance and gain peak curves have been calculated at different temperatures. Good agreement between experimental and theoretical results has been obtained.

Organic electronics have great potential due to their flexible structure, high performance, and their ability to build effective and low-cost photodetectors. We investigated the parameters of the P3HT and PCBM layers for device performance and optimization. SCAPS-1D simulations were employed to optimize the thicknesses of the P3HT and PCBM layers, investigate the effects of shallow doping in the P3HT layer, and assess the influence of the back contact electrode’s work function on device performance. Furthermore, this study explored the impact of interface defect layer density on the characteristics of the device. Through systematic analyses, the optimal parameters for enhancing device responsivity were identified. The findings indicate that a P3HT layer thickness of 1200 nm, a PCBM layer thickness of 20 nm, and a back contact electrode with a work function of 4.9 eV achieve the highest responsivity. Notably, at a bias of −0.5 V, the responsivity exceeds 0.4 A/W within the wavelength range of 450 nm to 630 nm. These optimized parameters underscore the significant potential of the developed device as an organic photodetector, particularly for visible light detection.