Optical losses represent one of the primary obstacles to increasing the efficiency of silicon solar cells. The recommended solution to minimize optical losses is the use of plasmonic metal nanoparticles; however, they act as recombination centers within the solar cell construction, leading to a decrease in performance. The goal of this article is to introduce cobalt/graphene nanoparticles into the solar cell to minimize the optical losses. An ultra-thin film silicon PIN solar cell of dimensions (400 ×400 ×900) nm3 with ring metal contact shape was designed and numerically investigated using COMSOL Multiphysics software version 6.2 by the finite element method (FEM). Core/shell cobalt-graphene (Co/Gr) nanoparticles are periodically introduced into the cell between two layers (electron transport and active) in a ratio of 50:50 with an inter-spacing of a similar diameter. The Co/Gr parameters, number of nanoparticles (2, 4, 6), radius (10, 20, 30) nm, and shell thickness (1, 2, 4) nm were extensively studied. In addition, the arrangement of the core/shell nanoparticle material was considered. The results manifest the best performance of the proposed cell at 4 nanoparticles of 30 nm radius with 2 nm shell thickness for Co/Gr nanoparticles to get a maximum photocurrent of 26.28 mA/cm2. It is concluded that the optical losses of the Co/Gr core/shell nanoparticles embedded in an ultra-thin film silicon solar cell are significantly reduced owing to the increment in the absorption and hence the photocurrent. This enhancement opens a new avenue for further improvements.
Article information: COVID-19 has roused the scientic community, prompting calls for immediate solutions to avoid the infection or at least reduce the virus's spread. Despite the availability of several licensed vaccinations to boost human immunity against the disease, various mutated strains of the virus continue to emerge, posing a danger to the vaccine's ecacy against new mutations. As a result, the importance of the early detection of COVID-19 infection becomes evident. Cough is a prevalent symptom in all COVID-19 mutations. Unfortunately, coughing can be a symptom of various of diseases, including pneumonia and inuenza. Thus, identifying the coughing behavior might help clinicians diagnose the COVID-19 infection earlier and distinguish
... Show MoreIn this paper, a new method of selection variables is presented to select some essential variables from large datasets. The new model is a modified version of the Elastic Net model. The modified Elastic Net variable selection model has been summarized in an algorithm. It is applied for Leukemia dataset that has 3051 variables (genes) and 72 samples. In reality, working with this kind of dataset is not accessible due to its large size. The modified model is compared to some standard variable selection methods. Perfect classification is achieved by applying the modified Elastic Net model because it has the best performance. All the calculations that have been done for this paper are in
An efficient combination of Adomian Decomposition iterative technique coupled Elzaki transformation (ETADM) for solving Telegraph equation and Riccati non-linear differential equation (RNDE) is introduced in a novel way to get an accurate analytical solution. An elegant combination of the Elzaki transform, the series expansion method, and the Adomian polynomial. The suggested method will convert differential equations into iterative algebraic equations, thus reducing processing and analytical work. The technique solves the problem of calculating the Adomian polynomials. The method’s efficiency was investigated using some numerical instances, and the findings demonstrate that it is easier to use than many other numerical procedures. It has
... Show MoreThe work was carried out in two stages. The first stage concerned
with study of silicon carbide (SiC) ratio (1.5, 2.5, 3.5, and 4.5 wt%)
effect on the Thermal conductivity of polyvinyl chloride (PVC); and
the second stage concerned with the UV – weatherizing (25, 50, and
75 hr), thermal aging (40, 50, and 60 °C), and rain- weatherizing (1,
2.5, and 4 hr) effect on the samples involved. Thermal conductivity
results proved that there was slight increase in thermal conductivity
by (SiC) loading; it increased from 0.17 W/m.K for PVC to 0.19
W/m.K for 4.5% SiC/PVC; where as it was systematically decreased
by UV- weatherizing, thermal aging, and rain- weatherizing. This
property is in a good agreement with gene
Background: The purpose of this study was to evaluate the effect of addition of surface treated silicon dioxide Nano filler (SiO2) on some properties of heat cured acrylic resin denture base material (PMMA). The properties were impact strength, transvers strength, and surface hardness. Materials and methods: In addition to controlled group SiO2 powder was added to PMMA powder by weight in three different percentages 3%, 5% and 7%, mixed by probe ultra-sonication machine.120 specimens were constructed and divided into 3 groups according to the test (each group consist of 40 specimens) and each group was subdivided into 4 sub-groups according to the percentage of added SiO2 (finally each subgroup consist of 10 specimens). The tests conducte
... Show MoreThis paper includes an experimental study of hydrogen mass flow rate and inlet hydrogen pressure effect on the fuel cell performance. Depending on the experimental results, a model of fuel cell based on artificial neural networks is proposed. A back propagation learning rule with the log-sigmoid activation function is adopted to construct neural networks model. Experimental data resulting from 36 fuel cell tests are used as a learning data. The hydrogen mass flow rate, applied load and inlet hydrogen pressure are inputs to fuel cell model, while the current and voltage are outputs. Proposed model could successfully predict the fuel cell performance in good agreement with actual data. This work is extended to developed fuel cell feedback
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