Dynamically vulcanized thermoplastic elastomer nanocomposites based on linear low-density polyethylene/styrene-butadiene rubber/nanoclay/bitumen: morphology and rheological behavior

Dynamically crosslinked thermoplastic elastomer nanocomposites were synthesized as modifier for the bitumen binder-based asphalts. Linear low-density polyethylene (LLDPE) and styrene-butadiene rubber (SBR), with the ratio of 80/20, bitumen, and organically modified clay (OC) were all melt mixed in the presence of the sulfur curing system. The proposed mixing was carried out in an internal mixer at 160 °C with a rotor speed of 120 rpm. To enhance the molecular interactions between the polymer phases and the clay silicate layers, maleic anhydride-grafted LLDPE (PE-g-MA) with the maleiation degree of 50% was also incorporated into the mixture. Observation of the composite samples, using the scanning electron microscopy (SEM), revealed the matrix dispersed type of morphology for all dynamically vulcanized samples. X-ray diffraction (XRD) and transmission electron microscopy (TEM) examinations evidenced the exfoliation of the clay silicate layers with good dispersion. Rheomechanical spectrometry (RMS) was performed on the prepared nanocomposites. All dynamically vulcanized nanocomposites comprising 2.5% of OC exhibited shear-thinning behavior and non-terminal characteristics with a low frequency range. These indicate the formation of three-dimensional physical networks by the clay nanolayers throughout the LLDPE matrix. The presence of the bitumen in the composition of the prepared nanocomposites improved the flowability of the samples. This is a promising feature of the prepared nanocomposites to be used as an elastic and resistant modifier in the composition of the bitumen-based asphalts.

     

Kinetic Model for Invertase‐Induced Sucrose Hydrolysis: Initial Time Lag

The kinetics of sucrose hydrolysis by invertase was studied in order to find a comprehensive model for the reaction pathway and mechanism. First, three common models of Michaelis‐Menten (MM), substrate inhibition (S²), and substrate clusters' inhibition (S³(I)) were investigated. The third model was found to better predict the initial sucrose concentration. Then, the S³(I) model was modified to cover the remaining pathway (S³(II)). Finally, a new comprehensive model (S³(III)) was evaluated, which in addition to what is considered in the two previously mentioned models (S³(I) and S³(II)) also involved the initial time lag. The model predictions showed an excellent agreement with the experimental data. The mean absolute error for the MM model is significantly reduced for the S³(III) model.     

Australian renewable energy progress

With some of the world's best solar and wind resources, Australia is a prime market for solar and wind energy. The growing renewable energy industry can take advantage of Australia's stable economy, good access to grid infrastructure and well organised financial and legal services.Although development has been slower than what was anticipated, but with the promises made by the new government, the renewable community hopes for a brighter future for solar and wind energy in Australia.The objective of this paper is to present an overview of the current status of solar and wind energy in Australia, then to take a closer look at solar and wind potential, current activities, and finally to discusses about Australian Government support and to predict the future outlook of solar and wind energy.     

Mesoscale finite element prediction of concrete failure

The present paper studies the failure of concrete from the mesoscopic point of view. Biphasic cubic concrete samples containing spherical aggregates embedded in a homogenized mortar have been simulated using standard finite element method. Linear elasticity and damage-plasticity hypotheses are considered for the aggregates and mortar, respectively. Various triaxial loading conditions are assumed for each sample to generate adequate discrete failure points within the stress space. In the next step, the approximated failure surfaces of specimens are constructed using the Delaunay triangulation technique. The effects of mesostructural features such as aggregate grading curve, aggregate volumetric share, and more importantly the controlling parameters of mortar’s damage-plasticity constitutive model have been investigated. Finally, the failure modes of some selected samples have been reported and discussed.     

Performance assessment of decision-making units using an adaptive neural network algorithm: one period case

This study proposes a nonparametric method based on adaptive neural network (ANN) technique for measuring efficiency of decision making units (DMUs) in one period case as a complementary tool for the ANN-based efficiency methods in the previous studies. In previous studies, there are needed to have large volume of data, and so the proposed method in this study is more applicable because it can be used for the cases which have no historical data. In fact, a limitative weakness of the ANN-based efficiency methods about applying them for these cases is removed. So, it can be a competitive method to the other common tools for measuring efficiency. By noting the importance of flexible manufacturing system, this study presents a decision-making model for optimization of operators’ allocation in cellular manufacturing system by computer simulation. The methodology is illustrated through its application on a previously reported dataset. It was found out that ANN provides more robust results and identifies more efficient units than the conventional methods since better performance patterns are explored.     

Predictions of micelle-water partition coefficients and retention in micellar electrokinetic chromatography from solute structure. 2. Fragmental constant approach

The fragmental constant approach (FCA) was used to calculate water-sodium dodecyl sulfate (SDS) micelle partition coefficients, K(mw), for uncharged solutes from their structure. Subsequently, the availability of K(mw) values allows prediction of retention factor, k, in micellar electrokinetic chromatography (MEKC) using the simple relationship k = K(mw)phi, where phi is the phase ratio. The FCA model describes a micelle-water partition coefficient as the sum of the partition coefficients of the constituent atomic/molecular fragments, measured by fragmental constant values, f (i), as well as correction factors to account for various "intramolecular effects" that cause deviations from the predicted partition coefficients as, log K(mw) = sum(n)(i=1)aif i+sum(m)(i=1)kiCm. The fragmental constants for a set of 41 fragments were determined using a training set of 229 aromatic solutes and 198 aliphatic compounds. The K(mw) of the aromatic compounds in the training set were determined by MEKC, while the K(mw) of the aliphatic solutes were estimated using the linear solvation energy relationship (LSER) for the SDS micelles. The fragments consisted of both aromatic fragments (i.e., directly attached to an aromatic ring) and aliphatic fragments. The FCA predictions agree nicely with the observed and LSER partition coefficient values, even for complex molecular structures such as beta-blocker drugs. The results show the great potential of the FCA for a priori prediction of retention behavior in MEKC from solute structure.