Fabrication and properties evaluation of chitosan/BaTiO3 composite membranes for the periodontitis treatment.

Periodontitis gradually damages the hard and soft tissues surrounding the tooth, leading to tooth loss. In recent years, the use of biomaterials in periodontitis treatment has expanded, including gels, nanoparticles, microparticles, fibers, and membranes. Among these, membranes have more clinical applications. Due to the ability of the piezoelectric material to regenerate damaged tissues, the aim of this study was to create piezoelectric composite membranes. To achieve this, Barium titanate powder (BaTiO3 powder)-a piezoelectric substance-was synthesized using the hydrothermal method and analyzed with X-ray diffraction (XRD) and Field emission scanning electron microscopy (FESEM). Four types of membranes were fabricated using solvent casting method: three composite membranes with chitosan matrix and BaTiO3 fillers (at 3%, 6%, and 9% weight), and one chitosan membrane without BaTiO3. The microstructure of the membrane surfaces, agglomeration of BaTiO3 in membranes, and hydrophilicity, antibacterial, and electrical properties of the membrane were also investigated. The results indicated that membranes containing 3 and 6% BaTiO3 had suitable surface structure for the periodontitis treatment. Agglomeration of BaTiO3 particles was higher in the membrane containing 9% BaTiO3. The large amount of BaTiO3 improved the antibacterial properties of the membranes. Additionally, the membranes containing BaTiO3 had high electrical properties, especially those with 3% and 6% BaTiO3. Therefore, composite membranes containing BaTiO3, especially membranes containing 6% BaTiO3, are more favorable options than those without BaTiO3 for periodontitis treatment.

Performance of machine learning algorithms in spectroscopic ellipsometry data analysis of ZnTiO3 nanocomposite.

In this research, the optical properties of the PVP: ZnTiO3 nanocomposite are studied using the spectroscopic ellipsometry technique. The preparation procedure of the ZnTiO3 nanocomposite is explained in detail. The absorbance/transmittance, surface morphology, structural information, chemical identification, and surface topography of the ZnTiO3 nanocomposite is studied using UV-Vis spectroscopy, field-emission scanning electron microscopy, Raman spectroscopy, Fourier transform infra-red, and atomic force microscopy, respectively. The ellipsometry method is used to obtain the spectra of the real and imaginary parts of the dielectric function and refractive index in the photon energy range of 0.59-4.59 eV. Moreover, using two machine learning algorithms, namely artificial neural network and support vector regression methods, the ellipsometric parameters ψ and Δ are analyzed and compared with non-linear regression. The error and accuracy of each three methods, as well as the time required for their execution, are calculated to compare their suitability in the ellipsometric data analysis. Also, the absorption coefficient was used to determine the band gap energy of the ZnTiO3 nanocomposite, which is found to be 3.83 eV. The second-energy derivative of the dielectric function is utilized to identify six critical point energies of the prepared sample. Finally, the spectral-dependent optical loss function and optical conductivity of the ZnTiO3 nanocomposite are investigated.

Effect of sulphidation process on the structure, morphology and optical properties of GO/AgNWs composites.

In this study, composite materials composed of graphene oxide (GO) synthesized by a modified Hummers' method and silver nanowires (AgNWs) synthesized by a modified polyol method were prepared. The prepared composites were subjected to sulfidation under the influence of H2S gas. Structural changes in the samples were evaluated using X-ray diffraction (XRD). The binding nature of the composite was characterized using FT-IR spectroscopy. Optical properties and band gap values were investigated using ultraviolet-visible (UV-Vis) spectroscopy. The morphology of the composites was analyzed by transmission electron microscopy (TEM). A simple method using H2S gas was applied for the sulphidation process of the samples. The sulfidation process was successful under the influence of H2S gas, resulting in an increased distance between the GO layers and a decrease in the band gap value for the composite post-sulfidation. In addition, AgNWs were observed to decompose into Ag2S nanoparticles under the influence of H2S gas. It was determined that the value of the band gap of the sample changes because of sulphidation.

Preparation of anisotropic AgNWs/PVA/Ag2S nanocomposites via a vapor-phase sulfidation process.

This study used a modified polyol technique to synthesize silver nanowires (AgNWs), which were subsequently mixed with polyvinyl alcohol (PVA) polymer and air-dried under ambient conditions. As a result, AgNWs/PVA nanocomposites with a concentration of 2% were prepared by a casting process. After that, the upper surface of the produced samples was treated with H2S gas, as a result of which asymmetric structures were formed depending on the gas concentration, exposure time and penetration into the layers. The structural, morphological, and optical properties of these asymmetric structures were analyzed. Changes in the sample structure were studied using X-ray diffraction (XRD), their optical properties were studied using ultraviolet-visible (UV-Vis), Raman spectroscopy, and their morphology using Transmission electron microscopy (TEM). A simple technique involving H2S gas was used for the sulfidation process of the samples, marking the first exposure of AgNW/PVA nanocomposites to such treatment. Examination of the structural and optical properties of the surfaces revealed clear differences in their physical properties after sulfidation. These obtained results were also supported by TEM images. Finally, the successful production of AgNWs/PVA/Ag2S anisotropic structure was achieved by this method.

Machine learning approach for predicting electrical features of Schottky structures with graphene and ZnTiO3 nanostructures doped in PVP interfacial layer.

In this research, for some different Schottky type structures with and without a nanocomposite interfacial layer, the current-voltage (I-V) characteristics have been investigated by using different Machine Learning (ML) algorithms to predict and analyze the structures' principal electric parameters such as leakage current (I0), barrier height ([Formula: see text]), ideality factor (n), series resistance (Rs), shunt resistance (Rsh), rectifying ratio (RR), and interface states density (Nss). The interfacial nanocomposite layer is made by composing polyvinyl-pyrrolidone (PVP), zinc titanate (ZnTiO3), and graphene (Gr) nanostructures. The Gaussian Process Regression (GPR), Kernel Ridge Regression (KRR), Support Vector Regression (SVR), and Artificial Neural Network (ANN) are used as ML algorithms. The ML techniques training data are obtained using the thermionic emission method. Finally, by comparing the experimental and predicted results, the performance of the different ML algorithms in predicting the electrical parameters of Schottky diodes (SDs) has been compared to find the optimized ML algorithm. The ML predictions of basic electrical parameters by almost all algorithms are in good agreement with the actual values, while the SVR model has predicted closer values to the corresponding actual ones. The obtained results show that the quantity of the leakage current and Nss for MS type SD decreases, and φB0 increases with the interfacial layer usage, especially with graphene dopant.

In situ preparation of g-C3N4 nanosheet/FeOCl: Achievement and promoted photocatalytic nitrogen fixation activity.

Climate change, global warming, and population growth have led researchers to use eco-sociable procedures for the N2 reduction reaction. It has discovered that N2 molecule can be transformed into NH3 in ambient circumstances with nanocomposites upon visible irradiation. In this research paper, a new visible-light-driven photocatalyst was constructed, with various weight percents of FeOCl particles (10, 20, 30, and 40%) that have adhered on NS-CN. Subsequently, multiple features of the nanocomposites were assayed in detail. The results illustrated that the NS-CN/FeOCl (20%) system has remarkable photoactivity in the NH4+ production reaction in comparison with the NS-CN and CN, which showed 2.5 and 8.6 higher activity, respectively. The durability of NS-CN/FeOCl (20%) system, as a substantial factor, was assayed for 5 recycles. Moreover, the effect of electron quenchers, pH of media, and solvent was studied. At last, a feasible Z-scheme mechanism for the remarkable improvement of N2 fixation efficiency was offered.