Effect of matrix−nanoparticle supramolecular interactions on the morphology and mechanical properties of polymer foams

In this study, we report the fabrication of supramolecular polymer nanocomposite foams with a uniform cell structure, high cell density and high expansion ratio using a soft matrix of poly(methyl acrylate‐co‐2‐hydroxyethyl methacrylate) and silica nanoparticle fillers, both functionalized with ureido‐pyrimidinone (UPy) supramolecular groups. Microcellular structures were formed using a batch foaming process at 90 °C under a 9 MPa nitrogen atmosphere. Nanocomposites were characterized and compared before and after the foaming process to investigate the effect of supramolecular interactions on the thermomechanical properties and morphology of the foams. TEM images revealed that while strong inter‐filler supramolecular interactions do not have a positive effect on their dispersion state, matrix?filler interactions derived from hydrogen bonding UPy motifs result in a rather uniform distribution of nanoparticles. Competing filler?filler and matrix?filler supramolecular interactions can be balanced and optimized by adjusting UPy populations along the chains and on the surface of nanoparticles. At a given chain functionality, increasing the nanoparticle loading up to an optimum concentration improves the mechanical properties and formability of the system. Above such concentration strong interactions between fillers, which are not compensated by the matrix, result in large aggregates and consequently undermine the material performance. Supramolecular polymer foams illustrate a similar thermal and viscoelastic behavior to that of neat samples but after foaming, due to the formation of a cellular structure and rearrangement or dissociation of UPy dimers under the foaming conditions, the elastic modulus is reduced. © 2018 Society of Chemical Industry     

Numerical modeling of non-Fourier heat transfer and fluid flow during plasma arc welding of AISI 304 stainless steel

ABSTRACT

This paper is an attempt to study the evolution of temperature profiles and weld pool geometry during plasma arc welding (PAW) by solving the transient Navier–Stokes and Energy equations. The analysis for an AISI 304 stainless steel rectangular plate was carried out using a flexible written program in Fortran. Due to the low accuracy of the Fourier heat transfer equation for short times and large dimensions, a non-Fourier form of heat transfer equation was used. Gaussian heat source is considered as the heat source model. The fluid flow in the molten pool is of interest because it can change the temperature distribution in and around the molten zone. The governing equations for fluid flow were solved by the finite-volume method in which the SIMPLE method was utilized for pressure–velocity coupling. The effects of heat conduction, fluid flow, and force actions at the weld pool were considered. Thermo-physical properties such as thermal conductivity, specific heat, and dynamic viscosity vary as a function of temperature. There are two mechanisms involved which actively cause heat transfer to the surroundings: radiation and convection heat transfer. The numerical results are compared to the experimental data. The results corroborate that the weld pool thickness in the cross section of PAW and the time taken by molten metal to reach the end of thick metal are in good agreement with the experimental measurements. Finally, the results obtained from the assumed Fourier heat transfer are compared for the same study.     

Daily Outflow Prediction by Multi Layer Perceptron with Logistic Sigmoid and Tangent Sigmoid Activation Functions

This paper discusses the use of artificial neural network (ANN) models for predicting daily flows from Khosrow Shirin watershed located in the northwest part of Fars province in Iran. A Multi-Layer Perceptron (MLP) neural network was developed using five input vectors leading to five ANN models: MLP1, MLP2, MLP3, MLP4, and MLP5. Two activation functions were used and they were logistic sigmoid and tangent sigmoid. The MLP_Levenberg–Marquardt (LM) algorithm was used for the training of ANN models. A 5-year data record, selected randomly, was used for ANN training and testing. The predicted outflow showed that the tangent sigmoid activation function performed better than did the logistic sigmoid activation function. The values of R ² and RMSE for MLP4 with the tangent sigmoid activation function for the validation period were equal to 0.89 and 1.7 m³/s, respectively. Appropriate input vectors for MLPs were determined by correlation analysis. It was found that antecedent precipitation and discharge with 1 day time lag as an input vector best predicted daily flows. Also, comparison of MLPs showed that an increase in input data was not always useful.     

Protective effects of four Iranian medicinal plants against free radical-mediated protein oxidation

The role of oxidative protein damages in the pathophysiology of human diseases is currently a topic of considerable interest as oxidised proteins has been implicated in a wide spectrum of clinical disorders. In this study, the antioxidant activities of four Iranian medicinal plants, namely Teucrium polium, Cyperus rotundus, Anethum graveolens and Nasturtium officinale against metal–catalysed protein oxidation were evaluated by pro-oxidant model (Fe²⁺/ascorbate) in rat liver homogenates. The addition of Fe²⁺/ascorbate to the liver homogenate significantly increased the extent of protein oxidation, such as protein carbonyl (PCO) formation and loss of protein-bound sulphydryl (P-SH) groups. Furthermore, the rates of reactive oxygen species (ROS) formation and lipid peroxidation (LPO) were also increased. The plant extracts showed inhibitory effects against PCO formation, P-SH oxidation, ROS formation and LPO to varying degrees. Based on this study, the order of antioxidant activity against protein oxidation was found to be: T. polium > C. rotundus > A. graveolens > N. officinale. The protective effects of each plant extract could be due to its polyphenolic content. In that respect, the T. polium extract with highest polyphenolic content has more antioxidant activity against protein oxidation.     

A Focus on Natural Variation for Abiotic Constraints Response in the Model Species Arabidopsis thaliana

Plants are particularly subject to environmental stress, as they cannot move from unfavourable surroundings. As a consequence they have to react in situ. In any case, plants have to sense the stress, then the signal has to be transduced to engage the appropriate response. Stress response is effected by regulating genes, by turning on molecular mechanisms to protect the whole organism and its components and/or to repair damage. Reactions vary depending on the type of stress and its intensity, but some are commonly turned on because some responses to different abiotic stresses are shared. In addition, there are multiple ways for plants to respond to environmental stress, depending on the species and life strategy, but also multiple ways within a species depending on plant variety or ecotype. It is regularly accepted that populations of a single species originating from diverse geographic origins and/or that have been subjected to different selective pressure, have evolved retaining the best alleles for completing their life cycle. Therefore, the study of natural variation in response to abiotic stress, can help unravel key genes and alleles for plants to cope with their unfavourable physical and chemical surroundings. This review is focusing on Arabidopsis thaliana which has been largely adopted by the global scientific community as a model organism. Also, tools and data that facilitate investigation of natural variation and abiotic stress encountered in the wild are set out. Characterization of accessions, QTLs detection and cloning of alleles responsible for variation are presented.     

Silver cluster supported on nitrogen-doped graphene as an electrocatalyst with high activity and stability for oxygen reduction reaction

An Ag₈ cluster deposited on three different types of nitrogen (N)-doped graphene was studied using density functional theory calculations with empirical pair potentials (DFT-D). Among the different kinds of N-doped graphene, the pyridinic-N₃ (P-N₃) type can act as the best anchor position to stabilize Ag₈. In addition, it is found that supported Ag₈ clusters show higher activity in oxygen reduction reaction compared to unsupported clusters due to significant decrease in O₂ adsorption energy and higher charge transfer to O₂. Electron transfer from Ag to O₂ leads to the elongation of the OO bond, which facilitates the breaking of this bond in the oxygen reduction reaction. All results suggest that N-doped graphene support can play a significant role in the chemical reactivity of a Ag₈ cluster in oxygen reduction reaction.