Nanostructure of montmorillonite barrier layers: A new insight into the mechanism of flammability reduction in polymer nanocomposites

This study describes the mechanism of flammability reduction in flame-retarded polymer matrix organo-montmorillonite reinforced nanocomposites. Morphologies of untested polymer nanocomposites and char residues formed by combustion in the mass loss calorimeter are characterized by XRD and TEM techniques. It is postulated that a combination of well-dispersed montmorillonite platelets and flame retardants in the polymer matrix provides nano-structured char formation. Initial montmorillonite dispersion in flame-retarded nanocomposites is found to be a major controlling factor on formed char nanostructures. An initially intercalated structure is invariantly converted to complete montmorillonite collapse whereas an initially exfoliated structure transforms to nano-structured chars demonstrating retained exfoliation or a new state of intercalation via incomplete collapse of montmorillonite layers. It is proposed that nano-structured char formation is the effective mechanism of flammability reduction, i.e. reduction in rate of heat release during combustion, in flame-retarded polymer nanocomposites.     

An attempt to understand the observed plateau of the magnetization in tetranuclear iron(II) complexes of thiacalixarene macrocycles

The next-nearest-neighbour (NNN) effect in tetranuclear iron(II) complexes of thiacalixarene macrocycles using the isotropic Heisenberg model has been investigated in order to understand its effects on the observed plateau of magnetization. Although NNN effect is generally very weak in these kinds of systems, it was calculated that its response is quite significant to the external perturbations in certain temperature regions. Using the isotropic Heisenberg exchange Hamiltonian, zero-field energy spectra have been calculated for this particular tetranuclear system. The average magnetic moments with and without next-nearest-neighbour interactions were also calculated. In order to verify the calculations, the results were compared with experimental data taken from the literature, whence, it is suggested that observed magnetic behaviour can be improved by taking into account the NNN effect.     

Minimizing makespan on a single machine subject to random breakdowns

We investigate optimal sequencing policies for the expected makespan problem with an unreliable machine, where jobs have to be reprocessed in their entirety if preemptions occur because of breakdowns. We identify a class of uptime distributions under which LPT minimizes expected makespan.     

Direct determination of manganese in vitamin-mineral tablets using solid sampling electrothermal atomic absorption spectrometry

Manganese in vitamin-minerals tablets was determined by solid sampling electrothermal atomic absorption spectrometry (SS-ETAAS) using three different calibration methods, namely calibration against aqueous standards, standard addition with aqueous standards on solid samples and calibration against solid certified standards. Samples were only finely ground and introduced directly into the furnace by means of solid autosampler system without any dissolving process. Effects of different calibration techniques, temperatures and heating rates of atomization and pyrolysis steps on the accuracy and precision of the analyte elements were investigated. After optimization of the experimental parameters, there is good agreement (at 95% confidence level) between the results obtained by solid sampling and those obtained by acid digestion of samples.     

Excess isentropic compressibility and speed of sound of the ternary mixture 2-propanol + diethyl ether + n-hexane and the constituent binary mixtures at 298.15 K

Speed of sound and densities of the ternary mixture 2-propanol + diethyl ether + n-hexane and also the binary mixtures 2-propanol + diethyl ether and 2-propanol + n-hexane have been measured at the entire composition range at 298.15 K. The excess isentropic compressibilities and the excess speed of the sound have been calculated from experimental densities and speed of sound. These excess properties of the binary mixtures were fitted to Redlich-Kister equation, while the Cibulka’s equation was used to fit the values related to the values to the ternary system. These excess properties have been used to discuss the presence of significant interactions between the component molecules in the binary mixtures and also the ternary mixtures. Speed of sound of the binary mixtures and the ternary mixture have been compared with calculated values from free length theory (FLT), collision factor theory (CFT), Nomoto’s relation (NR), Van Deal’s ideal mixing relation (IMR) and Junjie’s relation (JR). The results are used to compare the relative merits of these theories and relations in terms of the root mean square deviation relative (RMSD_r).     

Fabrication of SERS active gold nanorods using benzalkonium chloride,and their application to an immunoassay for potato virus X

The authors report on a robust method for the synthesis of gold nanorods (AuNRs) with tunable dimensions and longitudinal surface plasmon resonance. The method relies on seed-mediated particle growth in the presence of benzalkonium chloride (BAC) in place of the widely used surfactant cetyltrimethyl ammonium bromide (CTAB). Uniform AuNRs were obtained by particle growth in solution, and BAC is found to stabilize the AuNRs for >1 year. The SERS activity of the resulting AuNRs is essentially identical to that of CTAB-protected nanorods. The SERS activity of the BAC protected nanorods was applied to the quantitative analysis of potato virus X (PVX). The calibration plot for PVX is linear in the 10 to 750 ng?mL^?1 concentration range, and the detection limit is 2.2 ng?mL^?1.

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Graphical abstract SERS-active gold nanorods (AuNRs) have been prepared by using benzalkonium chloride as stabilization agent. Effects of chemical parameters on AuNRs have been explored and AuNRs were used in quantitative analysis of potato virus X (PVX).

     

Nanoclay and carbon nanotubes as potential synergists of an organophosphorus flame-retardant in poly(methyl methacrylate)

This study explores whether nanoparticles incorporated in polymers always act as synergists of conventional flame-retardant additives. For this purpose, two different filler nanoparticles, namely organically modified layered-silicate clay minerals or nanoclays and multi-walled carbon nanotubes, were incorporated in poly(methyl methacrylate) filled with an organophosphorus flame-retardant that acts through intumescence. Effective dispersion techniques specific to each nanoparticle were utilized and prepared samples were thoroughly characterized for their nanocomposite morphologies. Nanoclays were shown to outperform carbon nanotubes in respect of improving the fire properties of intumescent formulations assessed by cone calorimeter analysis. An intriguing explanation for the observed behaviour was the restriction of intumescence by strong carbon nanotube networks formed on the flaming surfaces during combustion contrary to enhanced intumescent chars by nanoclays. Carbon nanotubes surpassed nanoclays considering the thermal stability of intumescent formulations in thermogravimetry whereas mechanical properties were significantly superior with nanoclays to those with carbon nanotubes.     

A data mining approach to forecast behavior

This study presents a data mining analysis of forecasting patterns in a supply chain. Multiple customers who are auto manufacturers order from a large auto parts supplier. The auto manufacturers provide forecasts for future orders and update them before the due date. The supplier uses these forecasts to plan production in advance. The accuracy of the forecasts varies from customer to customer. We provide a framework to analyze the forecast performance of the customers. There are different complexities in forecasts that are captured from our data set. Daily flow analysis helps to transform data and obtain accuracy ratios of forecasts. Customers are then classified based on their forecast performances. We demonstrate the application of some recent developments in clustering and pattern recognition analysis to performance analysis of customers.