Enhanced photocatalytic and electrochemical performance of hydrothermally prepared NiO-doped Co nanocomposites

In this study, we synthesize nanostructured nickel oxide (NiO) and doped cobalt (Co) by combining nickel(II) chloride hexahydrate (NiCl₂.6H₂O) and sodium hydroxide (NaOH) as initial substances. We analyzed the characteristics of the product nanostructures, including their structure, optical properties, and magnetic properties, using various techniques such as x-ray diffraction (XRD), scanning electron microscopy (SEM), ultraviolet absorption spectroscopy (UV–Vis), Fourier transform infrared (FTIR) spectroscopy, and vibrating sample magnetometers (VSM). The NiO nanoparticles doped with Co showed photocatalytic activity in degrading methylene blue (MB) dye in aqueous solutions. We calculated the degradation efficiencies by analyzing the UV–Vis absorption spectra at the dye's absorption wavelength of 664 nm. It was observed that the NiO-doped Co nanoparticles facilitated enhanced recombination and migration of active elements, which led to more effective degradation of organic dyes during photocatalysis. We also assessed the electrochemical properties of the materials using cyclic voltammetry (CV) and impedance spectroscopy in a 1 mol% NaOH solution. The NiO-modified electrode exhibited poor voltammogram performance due to insufficient contact between nanoparticles and the electrolyte solution. In contrast, the uncapped NiO's oxidation and reduction cyclic voltammograms displayed redox peaks at 0.36 and 0.30 V, respectively.     

Enhanced Activation of Atrial Natriuretic Peptide (ANP) and Natriuretic Peptide Receptor-A (NPRA) in Chronic Cigarette Smoke-Induced Lung Inflammation in Experimental Rats

International Journal of Peptide Research and Therapeutics - We sought to determine the circulatory level of atrial natriuretic peptide (ANP) and its receptor (Natriuretic peptide receptor-A) on...     

Prediction of Higher Heating Value of Solid Biomass Fuels Using Artificial Intelligence Formalisms

The higher heating value (HHV) is an important property defining the energy content of biomass fuels. A number of proximate and/or ultimate analysis based predominantly linear correlations have been proposed for predicting the HHV of biomass fuels. A scrutiny of the relationships between the constituents of the proximate and ultimate analyses and the corresponding HHVs suggests that all relationships are not linear and thus nonlinear models may be more appropriate. Accordingly, a novel artificial intelligence (AI) formalism, namely genetic programming (GP) has been employed for the first time for developing two biomass HHV prediction models, respectively using the constituents of the proximate and ultimate analyses as the model inputs. The prediction and generalization performance of these models was compared rigorously with the corresponding multilayer perceptron (MLP) neural network based as also currently available high-performing linear and nonlinear HHV models. This comparison reveals that the HHV prediction performance of the GP and MLP models is consistently better than that of their existing linear and/or nonlinear counterparts. Specifically, the GP- and MLP-based models exhibit an excellent overall prediction accuracy and generalization performance with high (>0.95) magnitudes of the coefficient of correlation and low (<4.5 %) magnitudes of mean absolute percentage error in respect of the experimental and model-predicted HHVs. It is also found that the proximate analysis-based GP model has outperformed all the existing high-performing linear biomass HHV prediction models. In the case of ultimate analysis-based HHV models, the MLP model has exhibited best prediction accuracy and generalization performance when compared with the existing linear and nonlinear models. The AI-based models introduced in this paper due to their excellent performance have the potential to replace the existing biomass HHV prediction models.