The quasiparticle band structures and optical properties of ACdTS kesterite are investigated here on the basis of first-principles calculations, including the many-body effects theory, by using the GW plus Bethe-Salpeter equation. There were significant GW-quasiparticle corrections, over 0.9 eV, to the GGA-Kohn-Sham band gap. Our calculations also show that ACdTS kesterite had a small binding energy, exhibited optical absorption in the visible region, high minority carrier mobility, and large diffusion in length, rendering this material a promising candidate for solar cells. Based on these findings, we designed and implemented an ACdTS absorber in a thin-film solar cell (TFSC) structure. The new kesterite solar cell has a high efficiency of 11.6% with a low deficit in the output voltage. Moreover, a strategic combination between the particle swarm optimization approach and the ACdTS TFSC decorated with periodic nanowires is proposed to obtain significantly improved photovoltaic characteristics. The optimized design identifies a new pathway for a high conversion efficiency of 14%, far surpassing that provided by the conventional TFSC kesterite.

Skin detection is very challenging because of the differences in illumination, cameras characteristics, the range of skin colours due to different ethnicities and many other variations. New effective and accurate methodologies are developed for skin colour detection to easily identify human's skin colour threw databases which are specifically designed to assist research in the area of face recognition. One of these is the recently built SFA database that showed high accuracy for segmentation of face images. The approach described in this paper exploits skin and non-skin samples provided by SFA for skin segmentation on the basis of the well-known Euclidean and Manhattan distance metrics. Most importantly, the scheme proposed tries to segment facial colour images inside or outside SFA by means of skin samples belonging to SFA. Simulation results in both SFA and UTD colour face databases indicate that detection rates higher than 95% can be achieved with either measure.

In this work, an inverted PTB7:PC₇₀BM bulk heterojunction solar cells with the configuration of ITO/ZnO/ PTB7:PC₇₀BM / HTMs/Ag for various inorganic materials as a hole transport layer (ZnO, MoO₃, NiO, PEDOT: PSS, V₂O₅ and Cu₂O) are simulated by using the GPVDM software which is a free general-purpose tool for the simulation of opto-electronic devices. The influence of the thickness of both PTB7:PC₇₀BM and HTMs layers on the performance of the solar cell are investigated. The obtained results indicated that on regardless on the type of the inorganic material constituted the Hole Transport Material (HTM), the solar cell parameters can be improved by reducing the HTM thickness while the active layer optimum thickness is around 90 nm. The performance of the device with all inorganic materials used as HTM reaches the same levels as the PEDOT/PSS for the lower thickness (10 nm). As the thickness is increased, the electrical parameters are significantly enhanced by inserting cuprous oxide (Cu₂O) compared to the conventional PEDOT: PSS.

A new multispectral InGaZnO (IGZO) thin-film phototransistor (TF PT) based on a graded band-gap (GBG) SiGe capping layer with metallic nanoparticles (MNPs) is proposed. An accurate drain-current model is developed to investigate the device performances, where the optical characteristics under different light excitations (530 nm, 820 nm, and 1550 nm) are analyzed using the 3-D Finite-difference time-domain method (FDTD). It is found that the proposed device shows high photoresponse characteristics. Besides, it is revealed that the GBG configuration, MNPs spatial distribution and size can induce a complex behavior, which influences the device photoresponse over multiple spectral bands. Importantly, an iterative decision-maker framework based on the Multi-Objective Genetic Algorithm (MOGA) metaheuristic approach is implemented to design efficient multispectral IGZO TF PT. It is demonstrated that the proposed MOGA-based scheme paves the way for the designer to identify the appropriate GBG profile and MNPs spatial distribution for highly-responsive devices at selective Visible and IR wavelengths and to realize high-performance multispectral sensors. The proposed approach based on combining the proposed IGZO TF PT structure with MOGA metaheuristic computation opens up a new strategy for the design and experimental fabrication of high-performance multispectral optoelectronic devices.

In this paper, a new high-performance tunable band-selective (UV-Visible) photodetector (PD) based on RF sputtered a-SiC active layer is demonstrated. SiC thin-films were deposited on glass substrate by RF magnetron sputtering method at different sputter power values ranging from 60 W to 120 W. The samples morphological, structural, optical and photodetection properties were investigated by carrying out XRD, SEM, EDS, UV-Vis spectroscopy and photoresponse measurements. It was revealed that the sputtering power could modulate the optical behavior of a-SiC alloy, tuning favorable visible absorbance at high sputter power. This phenomenon is correlated with the influence of the RF power on the SiC film structural properties and compositions. Interestingly, measurements showed that a-SiC PD elaborated at 60 W of RF power can detect UV radiation with a high responsivity of 138 mA/W, low noise effects, superior detectivity of 7.8 × 10¹² Jones, while maintaining the visible blindness property. On the other hand, the prepared device at high sputtering power exhibits extended photoresponse characteristics, yielding 426 mA/W and 77 mA/W of responsivity values over UV and visible ranges, respectively. Therefore, the present investigation can provide a new strategy for the design and fabrication of photodetector devices based on SiC platform with broadband and solar-blind adjustable sensing purposes according to the desired application.

In this paper, the leaky acoustic microwaves (LAW) in a piezoelectric substrate (Lithium Niobate LiNbO3 Cut Y-X) were studied. The main method for this research was classification using a probabilistic neural network (PNN).The originality of this method is in the accurate values it provides. In our case, this technique was helpful in identifying undetectable waves, which are difficult to identify by classical methods. Moreover, all the values of the real part and the imaginary part of the coefficient attenuation with the acoustic velocity were classified in order to build a model from which we could easily note the Leaky waves. Accurate values of the coefficient attenuation and acoustic velocity for Leaky waves were obtained. Hence, in this study, the focus was on the interesting modeling and realization of acoustic microwave devices (radiating structures) based on the propagation of acoustic microwaves