Eco‐plastics: Morphological and mechanical properties of recycled poly(carbonate)‐crushed rubber (rPC‐CR) blends

Crushed tire rubber particles (CR) have been dispersed into a recycled poly(carbonate) matrix (rPC) to obtain an eco‐friendly plastic (EFP). A positive synergy was expected from the association of an elastomeric phase to a tough thermoplastic matrix, helping on the other hand to develop a plastic with low impact on the environment. Mechanical melt‐mixing alone cannot provide a suitable interface, and led to blends with poor mechanical properties. Consequently, we have investigated different strategies to improve the EFP properties: First, the rubber surface has been treated by flaming or washing with dichloromethane and second, two copolymers, poly(ethylene‐co‐ethyl acrylate‐tert‐hydroxyl methacrylate) (E‐EA‐MAH) and poly(ethylene‐co‐methyl acrylate‐ter‐glycidyl methacrylate) (E‐MA‐GMA), were used to compatibilize CR particles with rPC matrix by reactive melt‐mixing in an internal mixer. The resulting blends mechanical properties were studied through static tension experiments and interpreted to the light of electronic microscopy fractography analysis and nanoindentation experiments. Significant gain of mechanical properties can be obtained by decreasing CR size under 140 μm (especially for CR contents between 5 and 20% m/m). To reach similar properties with rubber particles of diameter over 140 μm (but under 350 μm), it is necessary to activate their surface by either dichloromethane washing or flaming. Additional use of a compatibilizer extends the plastic behaviour domain of the EFP. rPC‐20% w/w CR is the best alternative material of our study. POLYM. ENG. SCI., 47:1768–1776, 2007. © 2007 Society of Plastics Engineers     

Linear and nonlinear viscoelastic and viscoplastic analysis of asphalt binders with warm mix asphalt additives

Warm-Mix Asphalt (WMA) is a widely used product, which proved a contribution to the reduction in asphalt mixing and compaction temperatures. This reduction leads to lower fuel consumption and smoke emission in asphalt plants. Most of the characterisation of binders used in WMA has focused in the past on measuring linear viscoelastic properties and associated Superpave parameters. Several studies have shown that the average stresses and strains of the asphalt mixture remain mostly within the linear viscoelastic response. However, localised strains in the binder phase of the mixture could reach values high enough to induce nonlinear viscoelastic and viscoplastic deformations. Therefore, this study focuses on an experimental and analytical evaluation of linear, nonlinear viscoelastic and viscoplastic responses of selected binders modified for use in WMA. The first part of the paper analyses the linear viscoelastic material properties and their ability to evaluate permanent deformation resistance. Then, the non-recoverable creep compliance parameter obtained from the Multiple Stress Creep Recovery (MSCR) test is analysed to assess the nonlinear response and permanent deformation of asphalt binders. The paper utilises a nonlinear plasto-viscoelastic (NPVE) approach to assess and quantify the nonlinear plasto-viscoelastic response of binders by separating the recoverable and irrecoverable strains measured in the MSCR test. Two WMA additives were included in this study by mixing them with polymer-modified and unmodified asphalt binders. Analysis of results showed that the NPVE approach captured a higher percentage of recovery than the NLVE approach. However, binder’s performance evaluation and ranking did not change by adopting the NPVE approach. The nonlinear viscoelastic parameters provided insight on the behaviour of asphalt binders mixed with WMA additives during loading cycles. Sasobit showed higher influence than Advera on binders in resisting permanent deformation by increasing the recoverable strain during the unloading phase.     

A recursive state estimator in the presence of state inequality constraints

This paper proposes an optimal recursive estimator to estimate the states of a stochastic discrete time linear dynamic system when the states of the system are constrained with inequality constraints. The case when the constraints are strictly satisfied is treated independently from the case when some of the constraints are violated. For the first case, the well known Kalman filter estimator is used. In the second case, an algorithm which uses a series of successive orthogonalizations on the measurement subspaces is employed to obtain the optimal estimate. It is shown that the proposed estimator has several attractive properties such that it is an unbiased estimator. More importantly, compared to other estimator found in the literature, the proposed estimator needs less computational efforts, is numerically more stable and it leads to a smaller variance. To show the effectiveness of the proposed estimator, several simulation results are presented and discussed.     

Novel nano polymeric system containing biosynthesized core shell silver/silica nanoparticles for functionalization of cellulosic based material

An innovative strategy for functional finishing of cellulosic based materials is based on the incorporation of a thin layer of surface modifying systems (SMS) in the form of stimuli-sensitive nanogels containing combining metal nanoparticles and silica. The silver–silica core–shell nanoparticles (NPs) were synthesized by simple one pot chemical method. Silica/silver nanoparticles have been synthesized using low concentration of dextran as reducing and stabilizing agent and using ascorbic acid as antioxidant agent. The core–shell NPs were characterized for their structural, morphological, compositional and optical behaviour using X-ray diffraction, scanning electron microscopy and energy dispersive X-ray analysis. Stimuli-responsive nanogel was prepared by copolymerization of poly(N-isopropylacrylamide) with pullulan, results in a nanogel that is responsive to both temperature and pH, the nano-particulate hydrogel of poly-NiPAAm-pullulan copolymer was synthesized using surfactant-free emulsion method. The prepared nano-particles were used during the preparation steps of the pullulan nanogel to obtain nanogel/combining metal/silica NPs to produce a composite materials. The nanoparticle size in dry (collapsed) state is estimated at 250 nm by SEM and TEM, and effect of temperature and pH on gel-nanoparticles was investigated by DLS and UV–vis spectrophotometry. The incorporation of the nanoparticles to cellulosic material was done by a simple pad dry-cure procedure from aqueous nanoparticle dispersion that contained a cross-linking agent. This application method provided sufficient integrity to coating by maintaining the responsiveness of surface modifying system. The stimuli responsiveness of modified cellulosic materials has been confirmed in terms of regulating its water uptake in dependence of pH and temperature.     

Effect of extraction methods on kinetic,chemical composition and antibacterial activities of Tunisian Thymus vulgaris. L. essential oil

The present study aims to compare two innovative extraction techniques: microwave-assisted hydrodistillation (MAHD) and solvent-free microwave extraction (SFME) through traditional extraction techniques: hydrodistillation (HD) and steam distillation (SD) for their efficiency in the extraction of the volatile compounds from Tunisian Thymus vulgaris leaves; the kinetic, yield, composition and antibacterial activities of the essential oil were assessed in vitro. Results show that the essential oils extracted by microwaves were quantitatively (yield) similar to those obtained through the conventional methods, but qualitatively, essential oils analysed by gas chromatography–mass spectrometry (GC–MS) presented 17, 11, 11 and 8 compounds obtained through SFME, MAHD, SD and HD, respectively, mostly consisting of carvacrol (89.24–41.17%), followed by γ-terpinene (11.37–1.37%) and para-cymene (27.95–2.05%). The essential oils were screened for antibacterial activity against 5 microorganisms. All essential oils obtained by studied extraction methods showed the same resistance against Gram (?) and Gram (+) bacteria. The SFME method gave the best results: rapid kinetic of extraction (30 min vs. 35 min for MAHD, 120 min for SD, and 180 min for HD), less energy saving and cleanest process.     

On the prediction of gas hold-up in two-phase flow systems using an Euler–Euler model

We quantify the ability of the two-fluid Euler–Euler model to predict the overall gas hold-up during two-phase flow in vertical columns using a combination of experiments and simulations. Gas hold-up in a bubble column and gas hold-up in the less-frequently studied co-current flow are investigated. For homogeneous flow characterized by nearly uniform bubble size, Euler–Euler model predictions are within 10% of the experimental values for both modes of operation, if the bubble diameter supplied as input to the model is the average bubble diameter in the physical system. This also holds true for heterogeneous flow in bubble columns despite the presence of a broad distribution of bubble sizes, if turbulence and bubble swarm effects on momentum exchange between phases are properly accounted for. Swarm corrections adequate for bubble columns, are less successful for co-current heterogeneous flow, for which gas hold-up predictions are least accurate (average error of 22%).     

A decision model for operating two alternate energy generating systems

A solar energy generating system is to be supplemented by two conventional generating systems. A method for investigating the effect of various operating policies of the combined system on the productivity and availability is developed. Using a Markov-Chain approach for computing the long-run expectations of the objectives it was demonstrated that the use of a stand-by mode for one of the conventional systems was optimal. Numerical examples are presented to demonstrate the methodology both for special and more general cases.     

Unsupervised Bayesian image segmentation using orthogonal series

This paper deals with the problem of unsupervised image segmentation which consists in first mixture identification phase and second a Bayesian decision phase. During the mixture identification phase, the conditional probability density function (pdf) and the a priori class probabilities must be estimated. The most difficult part is the estimation of the number of pixel classes or in other words the estimation of the number of density mixture components. To resolve this problem, we propose here a Stochastic and Nonparametric Expectation-Maximization (SNEM) algorithm. The algorithm finds the most likely number of classes, their associated model parameters and generates a segmentation of the image by classifying the pixels into these classes. The non-parametric aspect comes from the use of the orthogonal series estimator. Experimental results are promising, we have obtained accurate results on a variety of real images.