Synthesis and design of polyurethane and its nanocomposites derived from canola-castor oil: Mechanical,thermal and shape memory properties

The recent global pandemic and its tremendous effect on the price fluctuations of crude oil illustrates the side effects of petroleum dependency more evident than ever. Over the past decades, both academic and industrial communities spared endless efforts in order to replace petroleum-based materials with bio-derived resources. In the current study, a series of shape memory polymer composites (SMPC's) was synthesized from epoxidized vegetable oils, namely canola oil and castor oil fatty acids (COFA's) as a 100% bio-based polyol and isophorone diisocyanate (IPDI) as an isocyanate using a solvent/catalyst-free method in order to eventuate polyurethanes (PU's). Thereafter, graphene oxide (GO) nanoplatelets were synthesized and embedded in the neat PU in order to overcome the thermomechanical drawbacks of the neat matrix. The chemical structure of the synthesized components, as well as the dispersion and distribution levels of the nanoparticles, was characterized. In the following, thermal and mechanical properties as well as shape memory behavior of the specimens were comprehensively investigated. Likewise, the thermal conductivity was determined. This study proves that synthesized PU's based on vegetable oil polyols, including graphene nanoparticles, exhibit proper thermal and mechanical properties, which make them stand as a potential candidate to compete with traditional petroleum-based SMPC's.     

Synthesis,characterization and application of a novel nanorod-structured organic–inorganic hybrid material as an efficient catalyst for the preparation of aminouracil derivatives

Research on Chemical Intermediates - In this work, a novel nanorod-structured organic–inorganic hybrid material namely nanorod-[SiO2-Pr-Im-SO3H][TFA] (N-[SPIS][TFA]) has been synthesized, and...     

Direct Synthesis of Peptide-Containing Silicones: A New Way to Bioactive Materials

A simple and efficient way to synthesize peptide-containing silicone materials is described. Silicone oils containing a chosen ratio of bioactive peptide sequences were prepared by acid-catalyzed copolymerization of dichlorodimethylsilane, hybrid dichloromethyl peptidosilane, and Si(vinyl)- or SiH-functionalized monomers. Functionalized silicone oils were first obtained and then, after hydrosilylation cross-linking, bioactive polydimethylsiloxane (PDMS)-based materials were straightforwardly obtained. The introduction of an antibacterial peptide yielded PDMS materials showing activity against Staphylococcus aureus. PDMS containing RGD ligands showed improved cell-adhesion properties. This generic method was fully compatible with the stability of peptides and thus opened the way to the synthesis of a wide range of biologically active silicones.     

A proper orthogonal decomposition variational multiscale meshless interpolating element-free Galerkin method for incompressible magnetohydrodynamics flow

In the recent decade, the meshless methods have been handled for solving most of PDEs due to easiness of the meshless methods. One of the popular meshless methods is the element-free Galerkin (EFG) method that was first proposed for solving some problems in the solid mechanics. The test and trial functions of the EFG are based on the special basis. Recently, some modifications have been developed to improve the EFG method. One of these improvements is the variational multiscale EFG procedure. In the current article, the shape functions of interpolation moving least squares approximation have been applied to the variational multiscale EFG technique for solving the incompressible magnetohydrodynamics flow. In order to reduce the elapsed CPU time of simulation, we employ a reduced-order model based on the proper orthogonal decomposition technique. The current combination can be referred to as the reduced-order variational multiscale EFG technique. To illustrate the reduction in CPU time used as well as the efficiency of the proposed method, we applied it for the two-dimensional cases.     

Asymptotic stabilization of a class of nonlinear SDEs

Nonlinear Dynamics - In this paper, we investigate the asymptotic stability of the probability density function (pdf) of the states of a class of nonlinear SDEs. We use the Detailed-balance...     

A unified model for electrostatic sensors in fluid media

Nonlinear Dynamics - We present a unified model of electrostatic sensors comprising cantilever microbeam resonators in fluid media. The model couples Euler–Bernoulli beam equation to the...     

Multicomponent reaction of amine,carbon disulfide,and fluoronitrobenzene via nucleophilic attack on the fluorinated carbon for the synthesis of nitrophenyl methylcarbamodithioates

In this paper, multicomponent reaction of amine, carbon disulfide and fluoronitrobenzene is reported for the synthesis of nitrophenyl methylcarbamodithioate derivatives. The method is based on the nucleophilic attack of the activated methylcarbamodithioate salt to fluoronitrobenzene. Several starting materials are tested and successfully produced the corresponding nitrophenyl methylcarbamodithioate. A possible mechanism for the reaction is suggested.     

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Pd-PEPPSI type complexes are widely used as precatalyst in a variety of organic reactions, including the Negishi, Kumada and Suzuki-Miyaura cross-coupling reactions. The aim of this research is to determine potential proposed reaction pathways 1, 2, or 2′ (See Schemes 1 and S1–S4 ) for Pd-PEPPSI precatalyst activation in the presence of ethylene glycol as a solvent also in the gas phase at Cam-B3LYP-D3 method nominated among eight DFT methods examined. There is also investigation into the impact of promoter bases (NaOEt, NaOⁱPr, NaO^tBu) on precatalyst activation of Pd-PEPPSI. Eventually, the most favorable proposed reaction pathway and promoter base for reducing Pd(II) to Pd(0) are predicted computationally. Notably, our findings are consistent with the organ Pd-PEPPSI type complexes that offer increased catalytic activity and provide basic information in the presence of solvents designing the monoligated Pd(0)-solvent.     

Synthesis of Novel Triazole Incorporated Thiazolone Motifs Having Promising Antityrosinase Activity through Green Nanocatalyst CuI-Fe3O4@SiO2 (TMS-EDTA)

In the present work, novel 5-((1-benzyl-1,2,3-triazol-4-yl)methoxybenzylidene)-2-(arylamino)thiazol-4-one thiazolone incorporated triazole derivatives have been designed as tyrosinase inhibitors. The compounds were synthesized through click reaction in good yield. Moreover, the antityrosinas activity of the synthesized derivatives was evaluated. In the search for establishing a click copper-catalyzed azide/alkyne cycloaddition (CuAAC) reaction under strict conditions, in terms of a novel air-stable, a recyclable and efficient magnetic catalyst was planned for new triazole derivatives as a well-organized copper iodide supported on the functionalized Fe₃O₄@SiO₂ core-shell (CuI/Fe₃O₄@SiO₂(TMS-EDTA) nanoparticles). The engineered nanocatalyst synthesized for the first time and characterized by different methods, including FT-IR spectroscopy, XRD, FESEM, EDX, TEM, TGA, and BET analysis. The excellent catalytic performance in ethanol with high surface area (351.7 m²g⁻¹) and short reaction time for diverse functional groups (120–200 min), no use of toxic solvents, reusability of the catalyst, and using eco-friendly conditions are the advantageous of this work. Moreover,the nanocatalyst can be used at least five times without any significant decrease in the yield of the reaction. The thiazolidine-triazole derivatives 9a , 9c , 9e , and 9 g showed promising tyrosinase inhibitory activity with IC₅₀ values in the range of 5.90–9.81 μM. The compounds were found to be considerably more potent tyrosinase inhibitors than the reference inhibitor kojic acid (IC₅₀ = 18.36 μM).     

Analytical solution of stochastic differential equation by multilayer perceptron neural network approximation of Fokker–Planck equation

The Fokker–Planck equation is a useful tool to analyze the transient probability density function of the states of a stochastic differential equation. In this paper, a multilayer perceptron neural network is utilized to approximate the solution of the Fokker–Planck equation. To use unconstrained optimization in neural network training, a special form of the trial solution is considered to satisfy the initial and boundary conditions. The weights of the neural network are calculated by Levenberg–Marquardt training algorithm with Bayesian regularization. Three practical examples demonstrate the efficiency of the proposed method.