Microwave-Assisted Graft Copolymerization of Cellulosic Fibers for Removal of Heavy Metal Ions from Aqueous Solution

Functional materials obtained from cellulosic biofibers have gained attention due to the growing demand for them in the field of wastewater remediation. In view of the technological significance of functionalized cellulosic biofibers in wastewater treatment, the present study is a green approach to functionalized cellulosic fibers through graft copolymerization under microwave irradiation. The grafted cellulosic polymers were subsequently subjected to heavy metal ion adsorption studies in order to assess their application in wastewater remediation. The effects of pH, contact time, temperature, and metal ion concentration were studied in batchwise adsorption experiments. The Langmuir, Freundlich, and Tempkin models were used to show the adsorption isotherms. The maximum monolayer capacities, q _m. calculated using the Langmuir isotherm for Zn²⁺, Cd²⁺, and Pb²⁺ were found to be 37.79, 69.68, and 96.81 mg/g respectively. The thermodynamic parameter ΔH° and ΔG° values for metal ion adsorption on functionalized cellulosic fibers showed that adsorption process was spontaneous as well as exothermic in nature.     

Molecular Insights into the Antistress Potentials of Brazilian Green Propolis Extract and Its Constituent Artepillin C

Propolis, also known as bee-glue, is a resinous substance produced by honeybees from materials collected from plants they visit. It contains mixtures of wax and bee enzymes and is used by bees as a building material in their hives and by humans for different purposes in traditional healthcare practices. Although the composition of propolis has been shown to depend on its geographic location, climatic zone, and local flora; two largely studied types of propolis: (i) New Zealand and (ii) Brazilian green propolis have been shown to possess Caffeic Acid Phenethyl Ester (CAPE) and Artepillin C (ARC) as the main bioactive constituents, respectively. We have earlier reported that CAPE and ARC possess anticancer activities, mediated by abrogation of mortalin-p53 complex and reactivation of p53 tumor suppressor function. Like CAPE, Artepillin C (ARC) and the supercritical extract of green propolis (GPSE) showed potent anticancer activity. In this study, we recruited low doses of GPSE and ARC (that did not affect either cancer cell proliferation or migration) to investigate their antistress potential using in vitro cell based assays. We report that both GPSE and ARC have the capability to disaggregate metal- and heat-induced aggregated proteins. Metal-induced aggregation of GFP was reduced by fourfold in GPSE- as well as ARC-treated cells. Similarly, whereas heat-induced misfolding of luciferase protein showed 80% loss of activity, the cells treated with either GPSE or ARC showed 60–80% recovery. Furthermore, we demonstrate their pro-hypoxia (marked by the upregulation of HIF-1α) and neuro-differentiation (marked by differentiation morphology and upregulation of expression of GFAP, β-tubulin III, and MAP2). Both GPSE and ARC also offered significant protection against oxidative stress and, hence, may be useful in the treatment of old age-related brain pathologies.     

Artificial neural system modeling of Monte Carlo simulations of polymers

In this work, a neural network was used to learn features in potential energy surfaces and relate those features to conformational properties of a series of polymers. Specifically, we modeled Monte Carlo simulations of 20 polymers in which we calculated the characteristic ratio and the temperature coefficient of the characteristic ratio for each polymer. We first created 20 rotational potential energy surfaces using MNDO procedures and then used these energy surfaces to produce 10000 chains, each chain 100 bonds long. From these results we calculated the mean-square end-to-end distance, the characteristic ratio and its corresponding temperature coefficient. A neural network was then used to model the results of these Monte Carlo calculations. We found that artificial neural network simulations were highly accurate in predicting the outcome of the Monte Carlo calculations for polymers for which it was not trained. The overall average error for prediction of the characteristic ratio was 4,82%, and the overall average error for prediction of the temperature coefficient was 0,89%.     

Solid-fluid transition in a granular shear flow

The rheology of a granular shear flow is studied in a quasi-2D rotating cylinder. Measurements are carried out near the midpoint along the length of the surface flowing layer where the flow is steady and nonaccelerating. Streakline photography and image analysis are used to obtain particle velocities and positions. Different particle sizes and rotational speeds are considered. We find a sharp transition in the apparent viscosity (eta) variation with rms velocity (u). Below the transition depth we find that the rms velocity decreases with depth and eta proportional to u(-1.5) for all the different cases studied. The material approaches an amorphous solidlike state deep in the layer. The velocity distribution is Maxwellian above the transition point and a Poisson velocity distribution is obtained deep in the layer. The results indicate a sharp transition from a fluid to a fluid + solid state with decreasing rms velocity.     

Flexible software reliability growth model with testing effort dependent learning process

A lot of importance has been attached to the testing phase of the Software Development Life Cycle (SDLC). It is during this phase it is checked whether the software product meets user requirements or not. Any discrepancies that are identified are removed. But testing needs to be monitored to increase its effectiveness. Software Reliability Growth Models (SRGMs) that specify mathematical relationships between the failure phenomenon and time have proved useful. SRGMs that include factors that affect failure process are more realistic and useful. Software fault detection and removal during the testing phase of SDLC depend on how testing resources (test cases, manpower and time) are used and also on previously identified faults. With this motivation a Non-Homogeneous Poisson Process (NHPP) based SRGM is proposed in this paper which is flexible enough to describe various software failure/reliability curves. Both testing efforts and time dependent fault detection rate (FDR) are considered for software reliability modeling. The time lag between fault identification and removal has also been depicted. The applicability of our model is shown by validating it on software failure data sets obtained from different real software development projects. The comparisons with established models in terms of goodness of fit, the Akaike Information Criterion (AIC), Mean of Squared Errors (MSE), etc. have been presented.     

Synthesis of symmetrical 1,5-disubstituted granatanines

A general entry into symmetrical 1,5-disubstituted granatanines that involves an alkylative ring-closure on a 2,6-bis enolate piperidine intermediate is described.     

Prediction of extrudate swell in polymer melt extrusion using an Arbitrary Lagrangian Eulerian (ALE) based finite element method

Accurate prediction of extrudate (die) swell in polymer melt extrusion is important as this helps in appropriate die design for profile extrusion applications. Extrudate swell prediction has shown significant difficulties due to two key reasons. The first is the appropriate representation of the constitutive behavior of the polymer melt. The second is regarding the simulation of the free surface, which requires special techniques in the traditionally used Eulerian framework. In this paper we propose a method for simulation of extrudate swell using an Arbitrary Lagrangian Eulerian (ALE) technique based finite element formulation. The ALE technique provides advantages of both Lagrangian and Eulerian frameworks by allowing the computational mesh to move in an arbitrary manner, independent of the material motion. In the present method, a fractional-step ALE technique is employed in which the Lagrangian phase of material motion and convection arising out of mesh motion are decoupled. In the first step, the relevant flow and constitutive equations are solved in Lagrangian framework. The simpler representation of polymer constitutive equations in a Lagrangian framework avoids the difficulties associated with convective terms thereby resulting in a robust numerical formulation besides allowing for natural evolution of the free surface with the flow. In the second step, mesh is moved in ALE mode and the associated convection of the variables due to relative motion of the mesh is performed using a Godunov type scheme. While the mesh is fixed in space in the die region, the nodal points of the mesh on the extrudate free surface are allowed to move normal to flow direction with special rules to facilitate the simulation of swell. A differential exponential Phan Thien Tanner (PTT) model is used to represent the constitutive behavior of the melt. Using this method we simulate extrudate swell in planar and axisymmetric extrusion with abrupt contraction ahead of the die exit. This geometry allows the extrudate to have significant memory for shorter die lengths and acts as a good test for swell predictions. We demonstrate that our predictions of extrudate swell match well with reported experimental and numerical simulations.