Multifunctional Dy2NiMnO6/Reduced Graphene Oxide Nanocomposites and Their Catalytic, Electromagnetic Shielding, and Electrochemical Properties.

Multifunctional nanocomposites have shown great interest in clean energy systems and environmental applications in recent years. Herein, we first reported the synthesis of Dy2NiMnO6 (DNMO)/reduced graphene oxide (rGO) nanocomposites utilizing a hybrid approach involving sol-gel and solvothermal processes. Subsequently, we investigated these nanocomposites for their applications in catalysis, electromagnetic interference shielding, and supercapacitors. A morphological study suggests spherical-shaped DNMO nanoparticles of an average size of 382 nm that are uniformly distributed throughout the surface without any agglomeration. The as-prepared nanocomposites were used as catalysts to investigate the catalytic reduction of 4-nitrophenol in the presence of NaBH4. DNMO/rGO nanocomposites demonstrate superior catalytic activity when compared with bare DNMO, with the rate of reduction being influenced by the composition of the DNMO/rGO nanocomposites. In addition, novel multifunctional DNMO/rGO was incorporated into polyvinylidene difluoride (PVDF) to develop a flexible nanocomposite for electromagnetic shielding applications and exhibited a shielding effectiveness of 6 dB with 75% attenuation at a frequency of 8.5 GHz compared to bare PVDF and PVDF-DNMO nanocomposite. Furthermore, the electrochemical performance of DNMO/rGO nanocomposites was investigated as an electrode material for supercapacitors, exhibiting the highest specific capacitance of 260 F/g at 1 A/g. These findings provide valuable insights into the design of DNMO/rGO nanocomposites with remarkable performance in sustainable energy and environmental applications.

Uncertainty and sensitivity analysis of deep learning models for diurnal temperature range (DTR) forecasting over five Indian cities.

In this article, the maximum and minimum daily temperature data for Indian cities were tested, together with the predicted diurnal temperature range (DTR) for monthly time horizons. RClimDex, a user interface for extreme computing indices, was used to advance the estimation because it allowed for statistical analysis and comparison of climatological elements such time series, means, extremes, and trends. During these 69 years, a more erratic DTR trend was seen in the research area. This study investigates the suitability of three deep neural networks for one-step-ahead DTR time series (DTRTS) forecasting, including recurrent neural network (RNN), long short-term memory (LSTM), gated recurrent unit (GRU), and auto-regressive integrated moving average exogenous (ARIMAX). To evaluate the effectiveness of models in the testing set, six statistical error indicators, including root mean square error (RMSE), mean absolute error (MAE), coefficient of correlation (R), percent bias (PBIAS), modified index of agreement (md), and relative index of agreement (rd), were chosen. The Wilson score approach was used to do a quantitative uncertainty analysis on the prediction error to forecast the outcome DTR. The findings show that the LSTM outperforms the other models in terms of its capacity to forget, remember, and update information. It is more accurate on datasets with longer sequences and displays noticeably more volatility throughout its gradient descent. The results of a sensitivity analysis on the LSTM model, which used RMSE values as an output and took into account different look-back periods, showed that the amount of history used to fit a time series forecast model had a direct impact on the model's performance. As a result, this model can be applied as a fresh, trustworthy deep learning method for DTRTS forecasting.

Construction of CdSe-AuPd quantum dot 0D/0D hybrid photocatalysts: charge transfer dynamic study with electrochemical analysis for improved photocatalytic activity.

The integration of semiconductor quantum dots and noble metal nanoparticles can efficiently couple numerous effects corresponding to the individual domains of the hybrid system for a variety of applications. Herein, we establish a direct correlation between the electronic band structure and optical band gap of monometallic and bimetallic alloy nanoparticle decorated CdSe quantum dots, which in turn regulate the charge shuttling dynamics in a quantum dot hybrid (QDH) system. Directly coupled Au, Pd, AuPd, and CdSe QDHs were prepared via a simple fabrication technique. The photoluminescence intensity of the QDHs was quenched compared to that of CdSe quantum dots with a maximumally diminished CdSe-AuPd system. Broadening of the absorbance peak along with a blue shift for QDHs confirm the interaction of the energy levels of the QDs and metal domains. AuPd decorated CdSe QDs demonstrate enhanced photocatalytic activity compared to their monometallic counterparts, which has made them interesting catalysts reported for the first time. Lifetime decay measurements, which isolated the individual charge-transfer steps, showed that a maximum amount of photoexcitons can be separated by bimetallic alloy decoration compared to monometallic ones. Cyclic voltammetry results offer insight into the change in the conduction band edge energy position for both monometallic and bimetallic incorporating semiconductor hybrid systems. Our findings reveal that photoexcited semiconductor quantum dots undergo charge equilibration when the QDs are in contact with metallic domains, influencing the shifting of the conduction band energy level of the hybrid to a more negative potential, and this is a maximum for the CdSe-AuPd hybrid, resulting in the best photocatalytic activity. Shuttling of electrons around the conduction band of CdSe and the Fermi level of the metallic domains is the main deciding factor for an efficient photocatalyst hybrid system.

In silico 3D structure prediction and hydrogen peroxide binding study of wheat catalase.

Catalase (CAT) is one of the most active enzyme catalysts found in plants, animals and in all aerobic microorganisms. The major function of the enzyme is to decompose H2O2, produced by cellular metabolic activities under normal and stressful conditions to water and oxygen. The present study involves 3D structure modeling of wheat catalase from Triticum aestivum by MODELLER9v7 and its binding study with H2O2. The Evaluation of the model was based on Discrete Optimized Protein Energy Score (DOPE). The structure was also validated using PROCHECK comprising of 95.0% amino acid residues in favored regions of Ramachandran plot, Verify3D and ProsA which confirm that the model is reliable. The 3D model of the molecule was found to consist of ten strands and seventeen helices having a common fold characterised by ß-pleated sheet flanked either side by helices. The docking study with H2O2 indicates that Gln352 and Arg353 are two important determinant residues in binding H2O2 as these residues have strong hydrogen bonding contacts with the substrate. These hydrogen-bonding interactions play a significant role in the stability of the complex.

Mercury induced phytotoxicity and oxidative stress in wheat (Triticum aestivum L.) plants.

The phytotoxicity and oxidative damage in response to different concentrations of Hg (0.0, 2.5, 5.0, 10 and 25 µM) were evaluated in wheat plants. The root and shoot growth, content of chlorophyll and total soluble protein declined at 10 and 25 µM Hg. Roots of the plant were more affected as compared to the shoot. The malondialdehyde (MDA) quantity enhanced in the roots of wheat plants treated with 10 and 25 µM Hg and in the leaves of plants treated with 25 µM Hg. The concentration of H2O2 decreased at low concentration and increased at high concentration of Hg. The induction of enzymatic antioxidants (catalase, CAT; ascorbate peroxidase, APX; peroxidase, POX and superoxide dismutase, SOD) was found in the roots and leaves of plants with increased concentration of Hg up to 10 µM and low activities of these enzymes were observed at 25 µM Hg. Also, the level of K, Ca and Mg declined in leaf tissues of Hg treated plants. Thus wheat plants exposed to lower concentrations of Hg did not experience any oxidative stress. However, on treatment with 10 µM Hg, the roots and leaves responded differently. Both the leaves and roots of plants treated with higher concentration of Hg were subjected to comparatively greater oxidative damage and demonstrated that the antioxidative components were not able to remove the stress due to higher concentration of Hg and thus might affect the productivity in wheat plants.