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Simultaneously improving the electrochemical methanol oxidation reaction (MOR) and hydrogen evolution reaction (HER) using the electrolysis technique is a significant yet challenging task. To tackle this, we report a colloidal synthesis of Cr-dopant induced crystal orientation and shape modulation in Ni2P nanocrystals (NCs) as an advanced bifunctional electrocatalyst for electrosynthesis of value-added formate from the MOR at the anode and hydrogen at the cathode. We demonstrate that a two-electrode overall methanol splitting (OMeS) system using Cr-doped Ni2P nanorods (NRs) as a bifunctional catalyst can achieve a lowest voltage of 1.16 V to reach a current density of 10 mA cm−2, compared to the cell voltage of 1.65 V for overall water splitting. Combined experimental and theoretical investigations revealed that the Cr-dopant induces shape modulation and crystal orientation in Ni2P, which favors the thermodynamics of the dehydrogenation process in the MOR and hydrogen adsorption in the HER, leading to enhanced electrocatalytic activities. Interestingly, a proof-of-concept solar-driven system fabricated using a commercial Si photovoltaic cell integrated with an OMeS cell employing bifunctional Cr-doped Ni2P NRs generated a stable photocurrent density of ∼12.3 mA cm−2 for 60 min., demonstrating its promise for energy-efficient and selective electrosynthesis, enabling the production of valuable chemicals and clean hydrogen in a sustainable manner. [ABSTRACT FROM AUTHOR]

This article discusses the effects of methyl cinnamate on GMR-Aβ42 D. melanogaster larvae, which are transgenic organisms for Alzheimer's disease. The study aims to understand the potential therapeutic effects of methyl cinnamate on the phototactic behavior and oxidative stress in larvae. The results show that GMR-Aβ42 larvae exhibit positive phototactic behavior and higher oxidative stress compared to wild type larvae. The researchers suggest further analysis of the effects of methyl cinnamate on these symptoms to potentially develop a plant-based therapeutic. [Extracted from the article]

Electrochemical water splitting plays a vital role in facilitating the transition towards a sustainable energy future by enabling renewable hydrogen (H2) production, energy storage, and emission‐free transportation. Developing earth‐abundant electrocatalysts with outstanding overall water‐splitting performance, excellent catalytic activity, and robust long‐term stability is highly important in the practical application of water electrolysis. Self‐supported electrocatalysts have emerged as the most appealing candidate for practical H2 production due to their increased active site loading, rapid mass and charge transfer, and strong interaction with the underneath conducting support. Additionally, these electrocatalysts also provide enhanced reaction kinetics and stability. Here, a comprehensive review of recent progress in developing self‐supported Fe‐based electrocatalysts for water splitting and selective oxidation reactions is presented with examples of oxyhydroxides, layered double hydroxides, oxides, chalcogenides, phosphides, nitrides, and other Fe‐containing electrocatalysts. A comprehensive historical development in the synthesis of self‐supported Fe‐based electrocatalysts is provided, with an emphasis on the various deposition methods and the choice of self‐supported conducting substrates considering large‐scale commercial applications. An overview of mechanistic understanding and approaches for enhanced H2 production are also presented. Finally, the challenges and opportunities associated with developing Fe‐based electrocatalysts for practical applications in water splitting and alternative oxidation reactions are discussed. [ABSTRACT FROM AUTHOR]

Background: In the present work, molecular docking analysis was conducted on proposed protein derivatives from the protein data bank using the Auto Dock for Docking programme. study on protein binding affinity using 1-acetyl-3-(2-aminophenyl)-5,5-diphenylimidazolidine- 2,4-dione derivatives and the GABA receptor. Even if a number of innovative anticonvulsants have been created in India, some types of seizures are still not adequately controlled by smaller side effects even after the use of modern and revolutionary treatments. Materials and Methods: Target-oriented virtual screening of ligands under investigation with adaptable molecular docking techniques. We can choose the most promising compounds for further study of various derivatives by evaluating the ligands' affinity to the GABAA active sites. Via the inhibitor GABAergic route, a number of 5,5-diphenylimidazolidine derivatives were designed and investigated in silico. Results: Using the AutoDock 4.2 programme, a number of molecules associated with 5,5-diphenylimidazolidine were investigated for molecular docking, acute prediction, and ADMET analysis. According to docking research, these molecules have at least one hydrogen bond stabilising them. The proposed composites were all investigated for Gamma Aminobutyric Acid (GABA) inhibitory exertion and all shown good in silico ADME and molecular docking results. Conclusion: One of the essential enzymes for biosynthesis in many natural ecosystems, Gamma Aminobutyric Acid (GABA) is present in both humans and animals. By meticulously replicating the prior pharmacological experiment's circumstances, we are able to compare the outcomes and talk about certain commonalities in how molecule fragments affect anticonvulsant action. Docking score, ADMET analysis, acute toxicity prediction, and structural location of ligands in the active GABAA site enzyme were found to positively correlate, supporting the viability of target-based virtual screening as a way to expedite pharmacological screening. [ABSTRACT FROM AUTHOR]

In this study, we report a straightforward method for synthesizing semi-cubical nanostructures of tungsten trioxide (WO3) loaded with reduced graphene oxide (rGO) via a hydrothermal approach. Furthermore, the systematic investigations were made towards the improved gas sensing capabilities of these nanostructures for acetone. The use of semi-cubical rGO/WO3 nanostructures demonstrate the provision of efficient gas diffusion routes via a meticulously arranged mesoporous framework. Hence, it leads to a significant improvement in the sensing response towards acetone. The analysis of the sensing capabilities demonstrates that the response of the sensor is influenced by variations in operating temperature and gas concentration. It is seen that the inclusion of rGO not only enhances the sensing response but also gives quick response and recovery, which are measured to be 12 and 33 s, respectively. The WO3 sensor loaded with 0.1 wt% rGO demonstrates a discerning reaction to acetone, exhibiting a high response of 93.60% than that of the unmodified WO3 sensor (76.30%). The gas sensing properties are explained through the role of rGO and the structural and morphological properties of the developed nanostructures. [ABSTRACT FROM AUTHOR]

Background: The serine/threonine kinase p38 Mitogen-Activated Protein (MAP) kinase is one of the most well studied kinases in the inflammatory process. The goal of this study was to see how a p38 MAP Kinase inhibitor (PDB ID-1A9U) affected the activity of a p38 MAP kinase implicated in inflammation. "Inflammation is a common feature of age-related neurodegenerative diseases in the Central Nervous System (CNS)." The p38 Mitogen-Activated Protein Kinase (MAPK) pathway regulates the synthesis of IL-1 and TNF. Materials and Methods: The study focused on molecular docking and ADME of the relationships that exist between [4-amino-3-(1H-indol-1-yl) phenyl] (4-hydroxyphenyl) methanone derivatives and p38 Mitogen-Activated Protein (MAP) kinase. Inhibition of p38 Mitogen-Activated Protein (MAP) kinase pathway, a series of [4-amino-3-(1H-indol-1-yl) phenyl] (4-hydroxyphenyl) methanone derivations were developed and evaluated in silico. The designed composites' medicine-likeness packets were predicted. Results: According to molecular docking experiments, all composites had improved interactions with the target protein and may be powerful drugs. The developed [4-amino-3-(1H-indol-1-yl) phenyl] (4-hydroxyphenyl) methanone analogues may be more effective and safer anti-inflammatory medicines. Conclusion: According to the findings of this investigation, p38 MAPK inhibitors alone are a unique therapeutic target for inflammatory disorders. p38 Mitogen-Activated Protein (MAP) Kinase is a critical serine/threonine kinase that has been well studied in the inflammatory process. [ABSTRACT FROM AUTHOR]

Germanium selenide (GeSe), a new 2D semiconductor material, is an attractive material due to its excellent optoelectronic properties, which hold tremendous promise in a wide range of applications, including thin‐film solar cells (TFSCs) and photoelectrochemical (PEC) water splitting. Several attempts have been made to date in theoretical studies, high‐quality GeSe material synthesis, evaluating absorber properties, and developing efficient TFSCs and PEC devices. Using existing device topologies for chalcogenide materials, TFSCs with 5.2% efficiency and a PEC device with 3.17% solar‐to‐hydrogen efficiency have been recently developed. To enable its potential in high performances of TFSCs and PEC devices for future large‐scale applications, further improvement in materials quality, device design, and development is required. In this regard, this review provides a comprehensive overview of current advances made in GeSe material development and applications in TFSCs and PEC devices. First, the fundamental properties of GeSe material, theoretical studies, as well as in‐depth synthesis methods, are outlined. Then, key developments in GeSe‐based TFSCs and PEC devices are discussed with an emphasis on device designs. Finally, the most prominent impediments to a fundamental understanding of materials are highlighted, and perspectives on future research directions for improving material quality and device efficiency are provided. [ABSTRACT FROM AUTHOR]

Social media platforms play a key role in fostering the outreach of extremism by influencing the views, opinions, and perceptions of people. These platforms are increasingly exploited by extremist elements for spreading propaganda, radicalizing, and recruiting youth. Hence, research on extremism detection on social media platforms is essential to curb its influence and ill effects. A study of existing literature on extremism detection reveals that it is restricted to a specific ideology, binary classification with limited insights on extremism text, and manual data validation methods to check data quality. In existing research studies, researchers have used datasets limited to a single ideology. As a result, they face serious issues such as class imbalance, limited insights with class labels, and a lack of automated data validation methods. A major contribution of this work is a balanced extremism text dataset, versatile with multiple ideologies verified by robust data validation methods for classifying extremism text into popular extremism types such as propaganda, radicalization, and recruitment. The presented extremism text dataset is a generalization of multiple ideologies such as the standard ISIS dataset, GAB White Supremacist dataset, and recent Twitter tweets on ISIS and white supremacist ideology. The dataset is analyzed to extract features for the three focused classes in extremism with TF-IDF unigram, bigrams, and trigrams features. Additionally, pretrained word2vec features are used for semantic analysis. The extracted features in the proposed dataset are evaluated using machine learning classification algorithms such as multinomial Naïve Bayes, support vector machine, random forest, and XGBoost algorithms. The best results were achieved by support vector machine using the TF-IDF unigram model confirming 0.67 F1 score. The proposed multi-ideology and multiclass dataset shows comparable performance to the existing datasets limited to single ideology and binary labels. [ABSTRACT FROM AUTHOR]

Silver nanoparticles (Ag-NPs) demonstrate unique properties and their use is exponentially increasing in various applications. The potential impact of Ag-NPs on human health is debatable in terms of toxicity. The present study deals with MTT(3-(4, 5-dimethylthiazol-2-yl)-2, 5-diphenyl-tetrazolium-bromide) assay on Ag-NPs. We measured the cell activity resulting from molecules' mitochondrial cleavage through a spectrophotometer. The machine learning models Decision Tree (DT) and Random Forest (RF) were utilized to comprehend the relationship between the physical parameters of NPs and their cytotoxicity. The input features used for the machine learning were reducing agent, types of cell lines, exposure time, particle size, hydrodynamic diameter, zeta potential, wavelength, concentration, and cell viability. These parameters were extracted from the literature, segregated, and developed into a dataset in terms of cell viability and concentration of NPs. DT helped in classifying the parameters by applying threshold conditions. The same conditions were applied to RF to extort the predictions. K-means clustering was used on the dataset for comparison. The performance of the models was evaluated through regression metrics, viz. root mean square error (RMSE) and R2. The obtained high value of R2 and low value of RMSE denote an accurate prediction that could best fit the dataset. DT performed better than RF in predicting the toxicity parameter. We suggest using algorithms for optimizing and designing the synthesis of Ag-NPs in extended applications such as drug delivery and cancer treatments. [ABSTRACT FROM AUTHOR]