Effect of solution pH, ionic strength, and temperature on adsorption behavior of reactive dyes on activated carbon

The adsorption behavior of C.I. Reactive Blue 2, C.I. Reactive Red 4, and C.I. Reactive Yellow 2 from aqueous solution onto activated carbon was investigated under various experimental conditions. The adsorption capacity of activated carbon for reactive dyes was found to be relatively high. At pH 7.0 and 298 K, the maximum adsorption capacity for C.I. Reactive Blue 2, C.I. Reactive Yellow 2 and C.I. Reactive Red 4 dyes was found to be 0.27, 0.24, and 0.11 mmol/g, respectively. The shape of the adsorption isotherms indicated an L2-type isotherm according to the Giles and Smith classification. The experimental adsorption data showed good correlation with the Langmuir and Ferundlich isotherm models. Further analysis indicated that the formation of a complete monolayer was not achieved, with the fraction of surface coverage found to be 0.45, 0.42, and 0.22 for C.I. Reactive Blue 2, C.I. Reactive Yellow 2 and C.I. Reactive Red 4 dyes, respectively. Experimental data indicated that the adsorption capacity of activated carbon for the dyes was higher in acidic rather than in basic solutions, and further indicated that the removal of dye increased with increase in the ionic strength of solution, this was attributed to aggregation of reactive dyes in solution. Thermodynamic studies indicated that the adsorption of reactive dyes onto activated carbon was an endothermic process. The adsorption enthalpy (?H) for C.I. Reactive Blue 2 and C.I. Reactive Yellow 2 dyes were calculated at 42.2 and 36.2 kJ/mol, respectively. The negative values of free energy (?G) determined for these systems indicated that adsorption of reactive dyes was spontaneous at the temperatures under investigation (298-328 K). © 2007 Elsevier Ltd. All rights reserved.

Predicting impact damage, residual strength and crashworthiness of composite structures

The development of the latest generation of wide-body carbon-fibre composite passenger aircraft has heralded a new era in the utilisation of these materials. The premise of superior specific strength and stiffness, corrosion and fatigue resistance, is tempered by high development costs, slow production rates and lengthy and expensive certification programmes. Substantial effort is currently being directed towards the development of new modelling and simulation tools, at all levels of the development cycle, to mitigate these shortcomings. One of the primary challenges is to reduce the extent of physical testing, in the certification process, by adopting a ‘certification by simulation’ approach. In essence, this aspirational objective requires the ability to reliably predict the evolution and progression of damage in composites. The aerospace industry has been at the forefront of developing advanced composites modelling tools. As the automotive industry transitions towards the increased use of composites in mass-produced vehicles, similar challenges in the modelling of composites will need to be addressed, particularly in the reliable prediction of crashworthiness. While thermoset composites have dominated the aerospace industry, thermoplastics composites are likely to emerge as the preferred solution for meeting the high-volume production demands of passenger road vehicles. This keynote presentation will outline recent progress and current challenges in the development of finite-element-based predictive modelling tools for capturing impact damage, residual strength and energy absorption capacity of thermoset and thermoplastic composites for crashworthiness assessments.

Use of a Neural Network to Predict Strength and Optimum Compositions of Natural Alumina-Silica-Based Geopolymers

In order to predict compressive strength of geopolymers prepared from alumina-silica natural products, based on the effect of Al 2 O 3 /SiO 2, Na 2 O/Al 2 O 3, Na 2 O/H 2 O, and Na/[Na+K], more than 50 pieces of data were gathered from the literature. The data was utilized to train and test a multilayer artificial neural network (ANN). Therefore a multilayer feedforward network was designed with chemical compositions of alumina silicate and alkali activators as inputs and compressive strength as output. In this study, a feedforward network with various numbers of hidden layers and neurons were tested to select the optimum network architecture. The developed three-layer neural network simulator model used the feedforward back propagation architecture, demonstrated its ability in training the given input/output patterns. The cross-validation data was used to show the validity and high prediction accuracy of the network. This leads to the optimum chemical composition and the best paste can be made from activated alumina-silica natural products using alkaline hydroxide, and alkaline silicate. The research results are in agreement with mechanism of geopolymerization.


Read More: http://ascelibrary.org/doi/abs/10.1061/(ASCE)MT.1943-5533.0000829

Integration Of Strength and Process Modelling of Friction-Stir-Welded Fuselage Panels

In order to reduce potential uncertainties and conservatism in welded panel analysis procedures, understanding of the relationships between welding process parameters and static strength is required. The aim of this study is to determine and characterize the key process induced properties of advanced welding assembly methods on stiffened panel local buckling and collapse performance. To this end, an in-depth experimental and computational study of the static strength of a friction stir welded fuselage skin-stiffener panel subjected to compression loading has been undertaken. Four welding process effects, viz. the weld joint width, the width of the weld Heat Affected Zone, the strength of material within the weld Heat Affected Zone and the magnitude of welding induced residual stress, are investigated. A fractional factorial experiment design method (Taguchi) has been applied to identify the relative importance of each welding process effect and investigate effect interactions on both local skin buckling and crippling collapse performance. For the identified dominant welding process effects, parametric studies have been undertaken to identify critical welding process effect magnitudes and boundaries. The studies have shown that local skin buckling is principally influenced by the magnitude of welding induced residual stress and that the strength of material in the Heat Affected Zone and the magnitude of the welding induced residual stress have the greatest influence on crippling collapse behavior.


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Causal conjunction fallacies: The roles of causal strength and mental resources

In two experiments we tested the prediction derived from Tversky and Kahneman's (1983) work on the causal conjunction fallacy that the strength of the causal connection between constituent events directly affects the magnitude of the causal conjunction fallacy. We also explored whether any effects of perceived causal strength were due to graded output from heuristic Type 1 reasoning processes or the result of analytic Type 2 reasoning processes. As predicted, Experiment 1 demonstrated that fallacy rates were higher for strongly than for weakly related conjunctions. Weakly related conjunctions in turn attracted higher rates of fallacious responding than did unrelated conjunctions. Experiment 2 showed that a concurrent memory load increased rates of fallacious responding for strongly related but not for weakly related conjunctions. We interpret these results as showing that manipulations of the strength of the perceived causal relationship between the conjuncts result in graded output from heuristic reasoning process and that additional mental resources are required to suppress strong heuristic output.

Influence of Ionic Strength and pH on the Limitation of Latex Microsphere Deposition Sites on Iron-oxide Coated Sand by Humic Acid

This paper advances findings of Yang et al. 2010 and reports on how slight changes in pH or Ionic strength can significantly alter particle behaviour in porous media, when humic acids have been deposited beforehand. .