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The effects of global warming highlight the urgent need for effective solutions to this problem. The electrification of society, which occurs through the widespread adoption of electric vehicles (EVs), is a critical strategy to combat climate change. Lithium-ion batteries (LIBs) are vital components of the global energy-storage market for EVs, and sodium-ion batteries (SIBs) have gained renewed interest owing to their potential for rapid growth. Improved safety and stability have also put solid-state batteries (SSBs) on the chart of top batteries in the world. This review examines three critical battery technologies: LIBs, SIBs, and SSBs. Although research has historically concentrated on heavier battery components, such as electrodes, to achieve high gravimetric density, binders, which comprise less than 5% of the battery weight, have demonstrated great promise for meeting the increasing need for energy storage. This review thoroughly examines various binders, focusing on their solubilities in water and organic solvents. Understanding binder mechanisms is crucial for developing binders that maintain strong adhesion to electrodes, even during volume fluctuations caused by lithiation and delithiation. Therefore, we investigated the different mechanisms associated with binders. This review also discusses failure mechanisms and innovative design strategies to improve the performance of binders, such as composite, conductive, and self-healing binders. By investigating these fields, we hope to develop energy storage technologies that are more dependable and efficient while also helping to satisfy future energy needs.

Tissue engineering aims to develop artificial human tissues by culturing cells on a scaffold in the presence of biochemical cues. Properties of scaffold such as architecture and composition highly influence the overall cell response. Electrospinning has emerged as one of the most affordable, versatile, and successful approaches to develop nonwoven nano/microscale fibrous scaffolds whose structural features resemble that of the native extracellular matrix. However, dense packing of the fibers leads to small-sized pores which obstruct cell infiltration and therefore is a major limitation for their use in tissue engineering applications. To this end, a variety of approaches have been investigated to enhance the pore properties of the electrospun scaffolds. In this review, we collect state-of-the-art modification methods and summarize them into six classes as follows: approaches focused on optimization of packing density by (a) conventional setup, (b) sequential or co-electrospinning setups, (c) involving sacrificial elements, (d) using special collectors, (e) post-production processing, and (f) other specialized methods. Overall, this review covers historical as well as latest methodologies in the field and therefore acts as a quick reference for those interested in electrospinning matrices for tissue engineering and beyond.

The India Meteorological Department (IMD) has been issuing long-range forecasts (LRF) based on statistical methods for the southwest monsoon rainfall over India (ISMR) for more than 100 years. Many statistical and dynamical models including the operational models of IMD failed to predict the recent deficient monsoon years of 2002 and 2004. In this paper, we report the improved results of new experimental statistical models developed for LRF of southwest monsoon seasonal (June-September) rainfall. These models were developed to facilitate the IMD's present two-stage operational forecast strategy. Models based on the ensemble multiple linear regression (EMR) and projection pursuit regression (PPR) techniques were developed to forecast the ISMR. These models used new methods of predictor selection and model development. After carrying out a detailed analysis of various global climate data sets; two predictor sets, each consisting of six predictors were selected. Our model performance was evaluated for the period from 1981 to 2004 by sliding the model training period with a window length of 23 years. The new models showed better performance in their hindcast, compared to the model based on climatology. The Heidke scores for the three category forecasts during the verification period by the first stage models based on EMR and PPR methods were 0.5 and 0.44, respectively, and those of June models were 0.63 and 0.38, respectively. Root mean square error of these models during the verification period (1981-2004) varied between 4.56 and 6.75% from long period average (LPA) as against 10.0% from the LPA of the model based on climatology alone. These models were able to provide correct forecasts of the recent two deficient monsoon rainfall events (2002 and 2004). The experimental forecasts for the 2005 southwest monsoon season based on these models were also found to be accurate.

Perovskite-based photo-batteries (PBs) have been developed as a promising combination of photovoltaic and electrochemical technology due to their cost-effective design and significant increase in solar-to-electric power conversion efficiency. The use of complex metal oxides of the perovskite-type in batteries and photovoltaic cells has attracted considerable attention. Because of its variable bandgap, non-rigid structure, high light absorption capacity, long charge carrier diffusion length, and high charge mobility, this material has shown promise in energy storage devices, especially Li-ion batteries (LIBs) and PBs. This review paper focuses on recent progress and comparative analysis of PBs using perovskite-based materials. The practical application of these batteries as dependable power sources faces significant technical and financial challenges because solar radiation is alternating. In order to address this, research is being performed on PBs with the integration of perovskite solar cells (PSCs) as a way to balance energy availability and demand, cut down on energy waste, and stabilize power output for wearable and portable electronics as well as energy storage applications.

Rice is the most important staple food crop feeding more than 50% of the world’s population. Rice blast is the most devastating fungal disease, caused by Magnaporthe oryzae (M. oryzae) which is widespread in rice growing fields causing a significant reduction in the yield. The present study was initiated to evaluate the effect of green synthesized silver nanoparticles (AgNPs) on the biochemical constituents of rice plants infected with blast. AgNPs were synthesized by using Azadirachta indica leaf extract and their characterization was performed using UV-visible spectroscopy, particle size analyser (PSA), scanning electron microscope (SEM), and X-ray diffraction (XRD) which confirmed the presence of crystalline, spherical shaped silver nanoparticles with an average size of 58.9 nm. After 45 days of sowing, artificial inoculation of rice blast disease was performed. After the onset of disease symptoms, the plants were treated with AgNPs with different concentrations. Application of nanoparticles elevated the activity of antioxidative enzymes such as superoxide dismutase, catalase, peroxidase, glutathione reductase, and phenylalanine ammonia-lyase compared to control plants, and total phenol and reducing sugars were also elevated. The outcome of this study showed that an increase in all biochemical constituents was recorded for A. indica silver nanoparticles-treated plants. The highest values were recorded in 30 ppm and 50 ppm AgNPs-treated plants, which showed the highest resistance towards the pathogen. Green synthesized AgNPs can be used in future for disease control in susceptible varieties of rice. The synthesized AgNPs using A. indica leaf extract have shown promising antibacterial activity when tested against 14 multidrug-resistant (MDR) bacteria comprising Gram-negative bacteria Escherichia coli (n = 6) and Klebsiella pneumoniae (n = 7) with a good zone of inhibition diameter, tested with the disc diffusion method. Based on these findings, it appears that A. indica AgNPs have promise as an antibacterial agent effective against MDR pathogens.

This work reveals a green combustion route for the synthesis of TiO 2 , Fe 2 O 3 and TiO 2 -Fe 2 O 3 nanocomposites as photocatalysts for decolorization of Titan Yellow (TY) and Methyl Orange (MO) dyes at room temperature in aqueous solution concentration of 20 ppm under UV-light irradiation. We observed that the TiO 2 -Fe 2 O 3 nanocomposite shows superior photocatalytic activity for TY dye compared to pure TiO 2 and Fe 2 O 3 . Rate constant (k) values of TiO 2 , Fe 2 O 3 and TiO 2 –Fe 2 O 3 for TY and MO are 0.0194, 0.0159, 0.04396 and 0.00931, 0.00772 0.0119 kmin −1 respectively. The surface area and pore volume of TiO 2 -Fe 2 O 3 nanocomposite were found to be 71.56 m 2 /g and 0.076 cm 3 /g, respectively as revealed by BET studies. From the Barrett–Joyner–Halenda (BJH) plot, the mean pore diameter of TiO 2 -Fe 2 O 3 nanoparticles was found to be 2.43 nm. Further, the TiO 2 -Fe 2 O 3 nanocomposite showed good electrochemical behavior as an electrode material for supercapacitors when compared to pure TiO 2 and Fe 2 O 3 nanoparticles resulted in stable electrochemical performance with nearly 100% coulombic efficiency at a scan rate of 10 mV/s for 1000 cycles. Interestingly, the novelty of this work is that the designed supercapacitors showed stable electrochemical performance even at 1000 th cycle, which might be useful for rechargeable supercapacitor applications. The electrochemical properties of the nanocomposites were compared by the data obtained by cyclic voltammograms, charge-discharge tests and electrochemical impedance spectroscopic studies. These results demonstrated that the TiO 2 -Fe 2 O 3 nanocomposite showed stable performance compared to TiO 2 and Fe 2 O 3 nanoparticles at current density of 5 Ag −1 .

The electrospinning technique allows engineering biomimetic scaffolds within micro to nanoscale range mimicking natural extracellular matrix (ECM). Chitosan (CS) and polycaprolactone (PCL) were dissolved in a modified solvent mixture consisting of formic acid and acetone (3:7) and mixed in different weight ratios to get chitosan-polycaprolactone [CS-PCL] blend solutions. The CS-PCL blend polymer was electrospun in the same solvent system and compared with PCL. The physicochemical characterization of the electrospun fibrous mats was done using scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), tensile test, swelling properties, water contact angle (WCA) analysis, surface profilometry and thermo gravimetric analysis (TGA). The CS-PCL fibrous mat showed decreased hydrophobicity. The CS-PCL mats also showed improved swelling property, tensile strength, thermal stability and surface roughness. The cytocompatibility of the CS-PCL and PCL fibrous mats were examined using mouse fibroblast (L-929) cell line by direct contact and cellular activity with extract of materials confirmed non-cytotoxic nature. The potential of CS-PCL and PCL fibrous mats as skin tissue engineering scaffolds were assessed by cell adhesion, viability, proliferation and actin distribution using human keratinocytes (HaCaT) and L-929 cell lines. Results indicate that CS-PCL is a better scaffold for attachment and proliferation of keratinocytes and is a potential material for skin tissue engineering.

ABSTRACT Background: The stromal vascular fraction (SVF) is an aqueous fraction isolated from the adipose tissue that constitutes different kinds of cells and extracellular matrix components. Hyaluronic acid (HA) is a linear polysaccharide in vertebrate tissues and is considered a potential tissue engineering scaffold due to its biocompatible nature. In this study, we have evaluated the cytotoxicity of xenofree HA in combination with an acellular component of adipose SVF (HA-aSVF) to propose it as a candidate biomaterial for future applications. Materials and Methods: 3-(4,5-dimethyl thiazolyl-2)-2,5-diphenyltetrazolium bromide assay of L-929 cells treated with HA-aSVF was used in our study. Data were normalized to cell control (untreated) and extracts of copper and ultra-high molecular weight polyethylene were used as positive (PC) and negative controls (NC). Results: Fibroblast cells retained the morphology after 24 h of treatment with HA-aSVF mixture and exhibited a similar percentage of cell activity compared to NC. PC showed a positive cytotoxic response as expected. The cells incubated with HA-aSVF showed a linear increase in cell activity indicating proliferation. Conclusion: The mixture of HA and acellular SVF in its flowable form is non-cytotoxic and showed improved cell proliferation. Hence the mixture can be proposed as a biomaterial and can be further explored for specific tissue engineering applications.

Introduction: Angiographic procedures are underused in patients with chronic kidney disease (CKD), who present with acute coronary syndromes, due to risk of contrast-induced acute kidney injury (CI-AKI). In this study, we assessed the change in estimated glomerular filtration rate (eGFR) over 3 months following coronary procedures in CKD patients. Methods: This observational study was done from July 2017 to January 2019 in patients undergoing elective coronary procedures with an eGFR <60 mL/min/1.73 m2. CKD-EPI equation was used to calculate eGFR pre and post coronary procedure at 24, 48, and 72 hours as well as 30, 90 days. AKI was diagnosed and patients were given prophylaxis for CI-AKI as per KDIGO recommendation (intravenous normal saline and oral N-acetyl cysteine). Results: Patients studied were 282 (225 males, 57 females) of which 68.1% were diabetics. Mean eGFR was 42.91 ± 10.51 mL/min/1.73 m2 and mean hemoglobin was 12.08 ± 1.51 gm/dL. Coronary angiogram (CAG) was done in 174; percutaneous transluminal coronary angioplasty (PTCA) was done in 108. Mean contrast volume in CAG was 55.17 ± 34.45 mL and in PTCA was 156.94±±47.99 mL. CI-AKI was seen in 66 (23.4%) patients. The incidence of CI-AKI increased with severity of underlying CKD. The variability of eGFR at 1 and 3 months after coronary procedures showed no significant change from baseline, even in the patients who developed CI-AKI. Conclusions: CI-AKI is self-limiting and has no major detrimental effects on eGFR at 1 and 3 months after contrast exposure.

The study of space exploration has been studied for a very long time, and as technology has developed, so too have the methods and techniques employed, along with the quantity and type of data acquired. We now receive so much astronomical data, and so much brand new data is being generated every day, that it is physically impracticable to examine it all only by human work. In our study, we look at a number of astronomers face while working with this massive amount of data, and they use deep learning techniques to discover the best data for each objective. [1]previously SVM, KNN, the random forest approach, decision trees, and other multi-class classification algorithms are all used in the methodology. Galaxies' propensity to belong to specific classes is forecasted using regression. even if the findings from the random forest method were the best it was unable to effectively divide galaxies into the five groups. This approach does not explain real-time categorization and does not take outliers into consideration. The model's adaptability is constrained. This categorization scheme is unable to account for the modelling of galaxies as well as their evolution. Here, we suggest using Inception v3 for categorization and VGG-19 for image analysis. Segmentation is a method for discovering and classifying galaxies. These techniques greatly advance certain fields of study where there are enormous volumes of duplicate data that must be deleted in accordance with the demands of the study, thanks to Python's high performance in the investigation of image processing and computer vision.. As a result, there is less of a need for researchers to carefully sort through all of the data that has been collected from satellite telescopes, sky surveys, etc. [2]