Catalytic Para‐Xylene Maximization. V. Toluene Methylation with Methanol and Disproportionation of Toluene Using Pt/ZSM‐5 and Pt/Mordenite Catalysts

Toluene was methylated with methanol and disproportionated using catalysts containing different Pt contents (0.2, 0.4 and 0.6%) supported on H‐ZSM‐5 or H‐mordenite (H‐M) zeolites in a fixed‐bed flow‐reactor operated atmospherically at temperatures of 300–500 °C in a flow of hydrogen. Platinum dispersion in the zeolite supports and acid sites strength distribution were evaluated using hydrogen chemisorption (1:1 stoichiometry) and ammonia temperature programmed desorption (TPD) in a differential scanning calorimeter (DSC). Toluene methylation was much faster on all catalysts than toluene disproportionation (DISP). Both reactions were more accelerated using H‐ZSM‐5 containing catalysts than H‐M containing catalysts. The yield of xylenes, and in particular para‐xylene, was significantly influenced by the yield of trimethylbenzenes (TMBs) in product. The selectivities for para‐, ortho‐ and meta‐xylenes production were found largely dependent on the Pt content in the catalysts, particularly when supported on H‐ZSM5‐zeolite. However, using Pt/H‐M catalysts, these selectivities were not strictly controlled by Pt content in the catalysts.     

Predicting Bitcoin (BTC) Price in the Context of Economic Theories: A Machine Learning Approach

Bitcoin (BTC)—the first cryptocurrency—is a decentralized network used to make private, anonymous, peer-to-peer transactions worldwide, yet there are numerous issues in its pricing due to its arbitrary nature, thus limiting its use due to skepticism among businesses and households. However, there is a vast scope of machine learning approaches to predict future prices precisely. One of the major problems with previous research on BTC price predictions is that they are primarily empirical research lacking sufficient analytical support to back up the claims. Therefore, this study aims to solve the BTC price prediction problem in the context of both macroeconomic and microeconomic theories by applying new machine learning methods. Previous work, however, shows mixed evidence of the superiority of machine learning over statistical analysis and vice versa, so more research is needed. This paper applies comparative approaches, including ordinary least squares (OLS), Ensemble learning, support vector regression (SVR), and multilayer perceptron (MLP), to investigate whether the macroeconomic, microeconomic, technical, and blockchain indicators based on economic theories predict the BTC price or not. The findings point out that some technical indicators are significant short-run BTC price predictors, thus confirming the validity of technical analysis. Moreover, macroeconomic and blockchain indicators are found to be significant long-term predictors, implying that supply, demand, and cost-based pricing theories are the underlying theories of BTC price prediction. Likewise, SVR is found to be superior to other machine learning and traditional models. This research’s innovation is looking at BTC price prediction through theoretical aspects. The overall findings show that SVR is superior to other machine learning models and traditional models. This paper has several contributions. It can contribute to international finance to be used as a reference for setting asset pricing and improved investment decision-making. It also contributes to the economics of BTC price prediction by introducing its theoretical background. Moreover, as the authors still doubt whether machine learning can beat the traditional methods in BTC price prediction, this research contributes to machine learning configuration and helping developers use it as a benchmark.     

Macrophage-Targeted Punicalagin Nanoengineering to Alleviate Methotrexate-Induced Neutropenia: A Molecular Docking,DFT, and MD Simulation Analysis

Punicalagin is the most bioactive pomegranate polyphenol with high antioxidant and free-radical scavenging activity and can potentially cure different ailments related to the cardiovascular system. The current research work was envisioned to predict the targeting efficiency of punicalagin (PG) nanoparticles to the macrophages, more specifically to bone marrow macrophages. For this, we selected mannose-decorated PLGA-punicalagin nanoparticles (Mn-PLGA-PG), and before formulating this nanocarrier in laboratory settings, we predicted the targeting efficiency of this nanocarrier by in silico analysis. The analysis proceeded with macrophage mannose receptors to be acquainted with the binding affinity and punicalagin-based nanocarrier interactions with this receptor. In silico docking studies of macrophage mannose receptors and punicalagin showed binding interactions on its surface. PG interacted with hydrogen bonds to the charged residue ASP668 and GLY666 and polar residue GLN760 of the Mn receptor. Mannose with a docking score of −5.811 Kcal/mol interacted with four hydrogen bonds and the mannose receptor of macrophage, and in PLGA, it showed a −4.334 Kcal/mol docking score. Further, the analysis proceeded with density functional theory analysis (DFT) and HOMO–LUMO analysis, followed by an extensive 100 ns molecular dynamics simulation to analyse the trajectories showing the slightest deviation and fluctuation. While analysing the ligand and protein interaction, a wonderful interaction was found among the atoms of the ligand and protein residues. This computational study confirms that this nanocarrier could be a promising lead molecule to regulate the incidence of drug-induced neutropenia. Furthermore, experimental validation is required before this can be stated with complete confidence or before human use.     

Limoniastrum monopetalum–Mediated Nanoparticles and Biomedicines: In Silico Study and Molecular Prediction of Biomolecules

An in silico approach applying computer-simulated models helps enhance biomedicines by sightseeing the pharmacology of potential therapeutics. Currently, an in silico study combined with in vitro assays investigated the antimicrobial ability of Limoniastrum monopetalum and silver nanoparticles (AgNPs) fabricated by its aid. AgNPs mediated by L. monopetalum were characterized using FTIR, TEM, SEM, and DLS. L. monopetalum metabolites were detected by QTOF–LCMS and assessed using an in silico study for pharmacological properties. The antibacterial ability of an L. monopetalum extract and AgNPs was investigated. PASS Online predictions and the swissADME web server were used for antibacterial activity and potential molecular target metabolites, respectively. Spherical AgNPs with a 68.79 nm average size diameter were obtained. Twelve biomolecules (ferulic acid, trihydroxy-octadecenoic acid, catechin, pinoresinol, gallic acid, myricetin, 6-hydroxyluteolin, 6,7-dihydroxy-5-methoxy 7-O-β-d-glucopyranoside, methyl gallate, isorhamnetin, chlorogenic acid, 2-(3,4-dihydroxyphenyl)-5,7-dihydroxy-4-oxo-4H-chromen-3-yl 6-O-(6-deoxy-β-l-mannopyranosyl)-β-d-glucopyranoside) were identified. The L. monopetalum extract and AgNPs displayed antibacterial effects. The computational study suggested that L. Monopetalum metabolites could hold promising antibacterial activity with minimal toxicity and an acceptable pharmaceutical profile. The in silico approach indicated that metabolites 8 and 12 have the highest antibacterial activity, and swissADME web server results suggested the CA II enzyme as a potential molecular target for both metabolites. Novel therapeutic agents could be discovered using in silico molecular target prediction combined with in vitro studies. Among L. Monopetalum metabolites, metabolite 12 could serve as a starting point for potential antibacterial treatment for several human bacterial infections.     

Determination of the Monoclonal Antibody Tocilizumab by a Validated Micellar Electrokinetic Chromatography Method

Chromatographia - Tocilizumab is a monoclonal antibody used in the treatment of several inflammatory and autoimmune diseases as well as cancers. Tocilizumab improves clinical outcomes and reduce...     

The Use of Laser Induced Breakdown Spectroscopy (LIBS) to Study Catalyst Deactivation of V2O5/γ‐Al2O3 as Compared to Inductively Coupled Plasma Optical Emission Spectrometry

Laser Induced Breakdown Spectroscopy (LIBS) method is introduced as a novel approach in this work to study catalyst deactivation of V₂O₅/γ‐‐Al₂O₃ for gas‐phase dehydration of glycerol and producing acrolein. The LIBS results of V₂O₅/γ‐Al₂O₃ samples are compared with those data that are obtained by Inductively Coupled Plasma Optical Emission Spectrometry (ICP‐OES). Experimental data of LIBS data specify that line intensities of vanadium are decreased by deactivation of V₂O₅/γ‐Al₂O₃ catalyst. A comparison between the results of LIBS test as well as ICP‐OES analysis shows that the amount of vanadium is decreased in the catalyst. Moreover, coke formation changes the surface of the catalyst. The results of deactivation of V₂O₅/γ‐Al₂O₃ are also compared with Pd/C catalyst deactivation.     

Experimental measurement and modeling study for estimation of wax disappearance temperature

Wax deposition is a frequent problem in oil pipelines and down-stream industries. Correct prediction of wax formation conditions is required to prevent this phenomenon. In this study, wax appearance temperature (WAT) of 12 Iranian oil and condensate samples were measured using viscometry data and differential scanning Calorimetry (DSC) analysis. Also, a new empirical correlation and intelligent artificial neural network (ANN) model were developed to estimate wax disappearance temperature (WDT) of crude oils. Specific gravity, pressure, and molecular weight of oil sample were used as input variables for these models. The ANN model was trained using different hidden neurons and training algorithms. Experimental measurements studies were used for validation of the new correlation. Comparing the results indicated that the ANN model has 0.27% error while most thermodynamic models have an average error of 0.35% to 2.19%. Also, the proposed correlation can predict WDT with good accuracy and minimum input data. Results show that this correlation has a maximum error of 1.16% for 310 published experimental data and 1.19% for 9 Iranian samples.     

Montmorillonite K10 Clay: An Efficient Catalyst for Hantzsch Synthesis of 1,4‐Dihydropyridine Derivatives

An efficient, solid‐catalyst‐mediated Hantzsch synthesis of 1,4‐dihydropyridines is described. This procedure has such advantages as short reaction time, high yields, and simple workup. The catalyst could be reused several times and keeps its initial activity in the subsequent reactions.     

Efficient,Rapid, and Regioselective Bromination of Phenols and Anilines with N-Bromosaccharin Using Tungstophosphoric Acid as a Heterogeneous Recyclable Catalyst

A simple, efficient, and rapid method for high-yielding regioselective mono bromination of phenols and anilines has been achieved by treatment with N-bromasaccharin in the presence of a catalytic amount of tungstophosphoric acid.     

An efficient four-component synthesis of dithiocarbamate derivatives

An environmentally benign, one-pot, four-component synthesis of chemically and pharmaceutically interesting dithiocarbamate derivatives is reported. The one-pot reaction of various aromatic aldehydes, ketones, aliphatic amines, and carbon disulfide, in the presence of potassium hydroxide in urea-choline chloride deep eutectic solvent or ethanol, leads to the corresponding dithiocarbamates in good to excellent yields. This method provides a convenient and time-saving strategy, with a simple work-up procedure.