Nanomaterial based drought mitigation in crops is through antioxidant defense system

Drought stress significantly impacts crop productivity by affecting the growth and development of plants. Studies have shown that drought stress induces oxidative damage, and the use of antioxidant molecules or nanoparticle (NPs) possessing antioxidant properties may decrease the negative effects of oxidative stress. So far, antioxidants like ascorbic acid, glutathione, proline, and glycine betaine have been studied in detail, but there is limited information available on the effect of NPs in decreasing drought induced oxidative damage. When plants are subjected to drought stress conditions, their ability to scavenge reactive oxygen species (ROS) decreases leading to an increase in ROS that can damage membranes, proteins, and lipids. Nonenzymatic antioxidants, such as tocopherols, ascorbate, glutathione, phenols, and carotenoids, along with enzymatic antioxidants such as superoxide dismutase, catalase, and ascorbate peroxidise, can strengthen the plant defense against ROS. Nanoparticles possessing antioxidant properties can mimic antioxidant enzymes, activate, and alter gene expression levels, leading to reduced ROS levels because of their increased surface area and presence of free electrons on their surface. This review discusses the effects of drought stress on crops, the synthesis, and unique properties of NPs, and the various traits improved by NPs possessing antioxidant properties to mitigate drought stress in plants

An efficient cathode electrocatalyst for anion exchange membrane water electrolyzer

\ua9 2024 The AuthorsA high performance and durable electrocatalyst for the cathodic hydrogen evolution reaction (HER) in anion exchange membrane (AEM) water electrolyzers is crucial for the emerging hydrogen economy. Herein, we synthesized Pt–C core-shell nanoparticles (core: Pt nanoparticles, shell: N-containing carbon) were uniformly coated on hierarchical MoS2/GNF using pyrolysis of h-MoS2/GNF with a Pt-aniline complex. The synthesized Pt–C core-shell@h-MoS2/GNF (with 11.3 % Pt loading) showed HER activity with a lower overpotential of 30 mV at 10 mA cm−2 as compared to the benchmark catalyst 20 % Pt–C (41 mV at 10 mA cm−2) with improved durability over 94 h at 10 mA cm−2. Furthermore, we investigated the structural stability and hydrogen adsorption energy for Pt13 cluster, C90 molecule, h-MoS2 sheet, Pt13–C90 core-shell, and Pt13–C90 core-shell deposited h-MoS2 sheets using density functional theory (DFT) simulations. We investigated the Pt–C core-shell@h-MoS2/GNF catalyst active sites during HER performance using in-situ Raman analysis as well as DFT. We fabricated AEM water electrolyzers with cathode catalysts of Pt–C core-shell@h-MoS2/GNF and evaluated device performance with 0.1 and 1.0 M KOH at 20 and 60 \ub0C. Our work provides a new pathway to design core-shell electrocatalysts for use in AEM water electrolyzers to generate hydrogen

An Enhanced Analysis of Traffic Intelligence in Smart Cities Using Sustainable Deep Radial Function

Smart cities have revolutionized urban living by incorporating sophisticated technologies to optimize various aspects of urban infrastructure, such as transportation systems. Effective traffic management is a crucial component of smart cities, as it has a direct impact on the quality of life of residents and tourists. Utilizing deep radial basis function (RBF) networks, this paper describes a novel strategy for enhancing traffic intelligence in smart cities. Traditional methods of traffic analysis frequently rely on simplistic models that are incapable of capturing the intricate patterns and dynamics of urban traffic systems. Deep learning techniques, such as deep RBF networks, have the potential to extract valuable insights from traffic data and enable more precise predictions and decisions. In this paper, we propose an RBF based method for enhancing smart city traffic intelligence. Deep RBF networks combine the adaptability and generalization capabilities of deep learning with the discriminative capability of radial basis functions. The proposed method can effectively learn intricate relationships and nonlinear patterns in traffic data by leveraging the hierarchical structure of deep neural networks. The deep RBF model can learn to predict traffic conditions, identify congestion patterns, and make informed recommendations for optimizing traffic management strategies by incorporating these rich and diverse data To evaluate the efficacy of our proposed method, extensive experiments and comparisons with real world traffic datasets from a smart city environment were conducted. In terms of prediction accuracy and efficiency, the results demonstrate that the deep RBF based approach outperforms conventional traffic analysis methods. Smart city traffic intelligence is enhanced by the model capacity to capture nonlinear relationships and manage large scale data sets.Comment: 25 pages, 6 figures, and 3 Table

CFD analysis of natural convection heat transfer in a static domestic refrigerator

Abstract Refrigerating compartment with and without shelves is designed for analyzing the air flow and temperature distributions by using the Simulation software Ansys work bench 14. Comparing the results of the air and temperature distributions inside the refrigerating compartment with and without shelves. Here shelves considered as obstacles and the refrigerator without freezer compartment is considered as a 2D rectangular enclosure one side is assumed as evaporator wall (cold wall) and other side as Door (warm wall). Inside the refrigerator cabin air flow takes by natural convection. Temperature distributions is observed in the compartment without shelves and with shelves and same is compared. In the natural convection heat transfer happens between the internal walls of the refrigerator and air by radiation, conduction and convection. Air flows downward near the cold wall and moves upward near warm wall due to the density variation. As a result, temperature stratification was observed in the refrigerating compartment. Evaporator wall maintains the colder region and door wall has the higher temperature.</jats:p

Synthesis and Machining Characterization of Copper-Multiwalled Carbon Nanotubes-Graphene Hybrid Composite Using SEM and ANOVA

In recent days nanotechnology has become one of the most excellent escalating technologies in the field of engineering and scientific areas. During the last decade there are numerous experimental analysis was carried out by many scholars on nanoparticles. This research work was carried out through different samples with varied composition of the nano-materials and the results were found on the optimisation of machining parameters of copper-multiwalled carbon nanotubes-graphene hybrids. The machining parameters of the hybrid composites were optimized using Taguchi method after the hybrid composite was made by stir casting process and analysis of variance (ANOVA) was used to analyse data and find the most influencing factor. The Taguchi’s signal to noise ratio was used is ‘smaller is better’. Confirmatory examinations were also performed for the purpose of validation after obtaining the optimized results. The hybrid nanocomposite specimens thus prepared were characterized by scanning electron microscope. From the results it was found that the addition of carbon nanotubes and graphene into copper leads to lower surface roughness values compared to pure copper.</jats:p