Three-dimensional (3D) printing has experienced a steady increase in popularity for direct manufacturing, where complex geometric items can be produced without the aid of templating tools, and manufacturing waste can be remarkably reduced. While customized medical devices and daily life items can be made by 3D printing of thermoplastics, microbial contamination has been a serious obstacle during their usage. A very clever approaches to overcome this challenge is to incorporate antimicrobial metal or metal oxide (M/MO) nanoparticles within the thermoplastics during or prior to 3D printing. Many M/MO nanoparticles can prevent contamination from a wide range of microorganism, including antibiotic-resistant bacteria via various antimicrobial mechanisms. Additionally, they can be easily printed with thermoplastic without losing their integrity and functionality. In this mini review, we summarize recent advancements and discuss future trends related to the development of 3D printed antimicrobial thermoplastic nanocomposites by addition of M/MO nanoparticles.
The search for stable and highly efficient solar cell absorbers has revealed interesting materials; however, the ideal solar cell absorber is yet to be discovered. This research aims to explore the potentials of dimethylammonium lead iodide (CH3NH2CH3PbI3) as an efficient solar cell absorber. (CH3NH2CH3PbI3) was modeled from the ideal organic–inorganic perovskite cubic crystal structure and optimized to its ground state. Considering the spin-orbit coupling (SOC) effects on heavy metals, the electronic band structure and bandgaps were calculated using the density functional theory (DFT). In contrast, bandgap correction was achieved by using the GW quasiparticle methods of the many-body perturbation theory. The optical absorption spectra were calculated from the real and imaginary dielectric tensors, which are determined by solving the Bethe–Salpeter equations of the many-body perturbation theory. Spin-orbit coupling induces band splitting and bandgap reduction in both DFT and GW methods, while the GW method improves the DFT bandgap. We report a DFT band gap of 1.55 eV, while the effect of spin-orbit coupling reduces the bandgap to 0.50 eV. Similarly, the self-consistent GW quasiparticle method recorded a bandgap of 2.27 eV, while the effect of spin-orbit coupling on the self-consistent GW quasiparticle method reported a bandgap of 1.20 eV. The projected density of states result reveals that the (CH3NH2CH3PbI3) does not participate in bands around the gap, with the iodine (I) p orbital and the lead (Pb) p orbital showing most prominence in the valence band and the conduction band. The absorption coefficient reaches 106 in the ultraviolet, visible, and near-infrared regions, which is higher than the absorption coefficient of CH3NH3PbI3. The spectroscopic limited maximum efficiency predicts a high maximum efficiency of about 62% at room temperature and an absorber thickness of about 10–1 to 102 μm, suggesting that (CH3NH2CH3PbI3) has an outstanding prospect as a solar cell absorber.
The application of machine learning algorithms to the detection of fraudulent credit card transactions is a challenging problem domain due to the high imbalance in the datasets and confidentiality of financial data. This implies that legitimate transactions make up a high majority of the datasets such that a weak model with 99% accuracy and faulty predictions may still be assessed as high-performing. To build optimal models, four techniques were used in this research to sample the datasets including the baseline train test split method, the class weighted hyperparameter approach, and the undersampling and oversampling techniques. Three machine learning algorithms were implemented for the development of the models including the Random Forest, XGBoost and TensorFlow Deep Neural Network (DNN). Our observation is that the DNN is more efficient than the other 2 algorithms in modelling the under-sampled dataset while overall, the three algorithms had a better performance in the oversampling technique than in the undersampling technique. However, the Random Forest performed better than the other algorithms in the baseline approach. After comparing our results with some existing state-of-the-art works, we achieved an improved performance using real-world datasets.
We have explicitly illustrated the challenges faced in creating input files for metallic materials in Quantum espresso by calculating the band structure of Ni and clarifying with the flags provided in this work. In this paper, we calculated the band structure of Ni after optimizations of the lattice constant, kinetic energy cutoff, ecutrho, k-points and described the basic parameters required for metallic materials. The input files provided in our work have been set such that the problem of spin/noncolin parameters and challenges faced by researchers trying to find the band structure of metallic materials have been solved. Some of these are presence of smearing, tprnfor, default nbnd, lspinorb, e.t.c.
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