Synthesis of unsaturated polyester resins for large sized composites

A series of unsaturated polyesters based on phthalic anhydride (PHA), maleic anhydride (MA), ethylene glycol (EG), diethylene glycol (DG), triethylene glycol (TG), propylene glycol (PG), styrene (Sty) and acrylonitrile (AN) were prepared. The molecular weights of the prepared polyesters were determined by end-group analysis. The effect of the structure of the resin on its curing behavior has been investigated. On the basis of the experimental study, the following were concluded: (1) The maximum curing temperature (T_max) is related to the molecular weight of the glycol incorporated in these castings. In this context the T_max was found to decrease with increasing the molecular weight. Meanwhile the time to peak temperature t_max was increasing. (2) The higher the percentage of AN in the crosslinking monomer system, the slower a resin cures. (3) The values of T_max were found to be influenced to a large extent by the percentage of AN.     

Thermal reaction of clCH2CONH2 in molten acetamide

Mixtures composed of amides and electrolytes exhibit interesting properties such as viscoelastic behaviour and a megavalue of the dielectric constant. In order to improve the dielectric properties, the authors are investigating a large number of molten mixtures of CF₃COONa with different types of amides, including the ternary system sodium trifluoroacetate-chloroacetamide-acetamide. The experimental measurements showed that the ternary mixtures are thermally unstable. A yellow oily product is formed; this was purified and tested by means of IR and NMR techniques. The formula and the kinetic mechanism are proposed.     

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.     

Automated and enhanced extraction of a small molecule-drug conjugate using an enzyme-inhibitor interaction based SPME tool followed by direct analysis by ESI-MS

We report a novel, fast, and automatic SPME-based method capable of extracting a small molecule-drug conjugate (SMDC) from biological matrices. Our method relies on the extraction of the drug conjugate followed by direct elution into an electrospray mass spectrometer (ESI-MS) source for qualitative and quantitative analysis. We designed a tool for extracting the targeting head of a recently synthesized SMDC, which includes acetazolamide (AAZ) as high-affinity ligand specific to carbonic anhydrase IX. Specificity of the extraction was achieved through systematic optimization. The design of the extraction tool is based on noncovalent and reversible interaction between AAZ and CAII that is immobilized on the SPME extraction phase. Using this approach, we showed a 330% rise in extracted AAZ signal intensity compared to a control, which was performed in the absence of CAII. A linear dynamic range from 1.2 to 25 μg/ml was found. The limits of detection (LOD) of extracted AAZ from phosphate-buffered saline (PBS) and human plasma were 0.4 and 1.2 μg/ml, respectively. This with a relative standard deviation of less than 14% (n = 40) covers the therapeutic range.

Graphical abstract

     

Semiquantitative determination of thirty cations by circular thin-layer chromatography

Summary A comprehensive scheme for the semiquantitative determination of 30 metal ions (Ag⁺, Pb²⁺, W⁶⁺, Tl⁺, Fe³⁺, Au³⁺, Mo⁶⁺, Ga³⁺, Sb⁵⁺, Ge⁴⁺, Te⁴⁺, Pt⁴⁺, Pd²⁺, Ru³⁺, Rh³⁺, Cu²⁺, Co²⁺, Ni²⁺, Zn²⁺, Cd²⁺, Cr⁶⁺, Mn²⁺, U⁶⁺, V⁵⁺, Sn⁴⁺, Bi³⁺, Hg²⁺, Ti⁴⁺, Se⁴⁺ and As³⁺) is described. It is based on circular thin-layer chromatographic technique. The error is ± 5% and 16 other metal ions, not included in the analysis scheme because of the non-availability of spray reagents, do not interfere. The determination of each cation has been made with different spray reagents separately and the mean error has been calculated. The analysis of the same cation with different spray reagents provides a useful check on the results. The method holds an excellent promise for the study of airborne particulates and for the determination of trace elements from blood, tissue and bone.

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Zusammenfassung Ein umfassender Analysengang für die halb quantitative Bestimmung von 30 Metallionen wurde beschrieben. Die Ringchromatographie auf Dünnschichten wurde hierzu verwendet. Der Fehler beträgt ±5%. 16 weitere Metallionen, die in Ermangelung geeigneter Sprühreagenzien in den Analysengang nicht aufgenommen wurden, stören nicht. Die Bestimmung jedes einzelnen Kations wurde mit verschiedenen Sprühmitteln vorgenommen; der mittlere Fehler wurde dann berechnet. So ist eine gute Überprüfung der Ergebnisse möglich. Das Verfahren dürfte sich für die Untersuchung der Luftverschmutzung sowie für die Bestimmung von Spurenelementen in Blut, Gewebe und Knochen eignen.

     

Modeling of H2 and H2/CH4 Moderate-Pressure Microwave Plasma Used for Diamond Deposition

One-dimensional transport models of moderate-pressure H ₂ and H ₂ /CH ₄ plasmas obtained in a diamond deposition microwave reactor are presented. These models describe the plasma as a thermochemically nonequilibrium flow with three different energy modes. The solution of the one-dimensional plasma transport equations enabled the estimation of plasma species concentrations and temperatures on the axis of the reactor. As far as pure H ₂ plasmas are concerned, results showed that the model predictions of gas and vibration temperatures are in good agreement with experimental measurements. The model also yields a relatively good qualitative prediction of the variations of H-atom mole fraction with the power density absorbed by the plasma. The results obtained for H ₂ /CH ₄ discharges showed that the model prediction on the variations of H-atom mole fraction with methane percentage in the discharge is in good qualitative agreement with experimental results. They also showed that methane is rapidly converted to acetylene before reaching the discharge zone. The concentrations of neutral hydrocarbon species in the reactor are mainly governed by thermal chemistry. The addition of methane strongly affects the ionization kinetics of the plasma. Three major ions are generally obtained in H ₂ /CH ₄ plasmas: C ₂ H ₂ ⁺ , C ₂ H ₃ ⁺ , and C ₂ H ₅ ⁺ . The relative predominance of these ions depends on the considered plasma region and on the discharge conditions. The ionic species concentrations are also mainly governed by chemistry, except very near the substrate surface. Finally the use of this transport model along with the surface chemistry model of Goodwin ⁽¹⁾ enabled us to estimate the diamond growth rate for several discharge conditions.     

N-halogeno-compounds. Part 9 [1]. Azoxy- and azo-arenes derived from 4-(dichloroamino)tetrafluoropyridine; crystal structure of trans-2,3,5,6-tetrafluoro-4-(2,4,6-trimethylphenyl-onn-azoxy)pyridine

The nitrosoarenes ArNO (Ar = C₆H₅, 2-MeC₆H₄, 2,4,6- Me₃C₆H₂ and C₆F₅) have been condensed with 4-(dichloroamino)- tetrafluoropyridine to provide the azoxy-compounds py_FN$\text{N}\text{+}$($\text{N}\text{-}$)Ar (py_F = 2,3,5,6-tetrafluoro-4-pyridyl); de-oxygenation of the first three with triphenylphosphine or triethyl phosphite gave the corresponding azo-compounds, and the reverse reaction was achieved in the case of py_FNNC₆H₂Me₃-2,4,6 using peroxytrifluoroacetic acid. Thermolysis of 4-azidotetrafluoropyridine in the presence of pentafluoronitrosobenzene provided the perfluorinated azoxy-compound py_FN$\text{N}\text{+}$($\text{O}\text{-}$)C₆F₅. X-Ray methods have been used to determine the molecular geometry of py_FN$\text{N}\text{+}$($\text{O}\text{-}$)C₆H₂Me₃-2,4,6.     

Understanding the dehydrogenation mechanism over iron nanoparticles catalysts based on density functional theory

The conversion of chemical feedstock materials into high value-added products accompanied with dehydrogenation is of great value in the chemical industry.However,the catalytic dehydrogenation reaction is inhibited by a limited number of expensive noble metal catalysts and lacks understanding of dehydrogenation mechanism.Here,we report the use of heterogeneous non-noble metal iron nanoparticles(NPs) incorporated mesoporous nitrogen-doped carbon to investigate the dehydrogenation mechanism based on experiment observation and density functional theory(DFT) method.Fe NPs catalyst displays excellent performance in the dehydrogenation of 1,2,3,4-tetrahydroquinoline(THQ)with 100% selectivity and 100% conversion for 10-12 h at room temperature.The calculated adsorption energy implies that THQ prefers to adsorb on Fe NPs as compared with absence of Fe NPs.What is more,the energy barrier of transition state is relatively low,illustrating the dehydrogenation is feasible.This work provides an atomic scale mechanism guidance for the catalytic dehydrogenation reaction and points out the direction for the design of new 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.