Gemini Cationic Surfactants: Synthesis and Influence of Chemical Structure on the Surface Activity

Three cationic gemini surfactants were synthesized and characterized using different methods. Their surface activities were measured using surface and interfacial tension measurements. The effect of the spacer chain length on the surface activity, emulsification power and interfacial tension was studied. The thermodynamic parameters showed the tendency towards micellization and adsorption. The results showed that longer spacers increased the micellization tendencies of the surfactants, while shorter spacers increased the adsorption tendency at the air–water interface.     

Computer-supported reflective learning: how apps can foster reflection at work

ABSTRACT

This paper discusses the potential of ICT to support reflective learning for professionals. We aggregated data collected in 20 field studies with 12 different applications, involving a total of 321 participants. The applications addressed individual reflection as well as collaborative reflection. Such a systematic analysis with different applications used across industry sectors and companies is unique in the current literature on computer-supported reflective learning in the workplace. Primarily, we assessed the reaction to reflection applications and their effectiveness regarding learning, behaviour change, and organisational impact. In addition, we investigated differences with respect to work experience. Results show that users had a positive reaction to the apps and perceived their use to be beneficial for their work by using them. In collaborative reflection an inexperienced employee can benefit from the experiences and perspectives of more experienced (co-)workers. In contrast, individual reflection was more profitable for more experienced workers. Notwithstanding the overall positive results, the actual implementation of reflection applications requires careful adaptation to the specific organisational and situational context, as well as introductory and accompanying measures to assure efficient and beneficial usage of the tools.     

Identifying the parameters of different configurations of photovoltaic models based on recent artificial ecosystem-based optimization approach

Identifying accurate and precise photovoltaic models' parameters is the primary gate in providing a proper PV system design simulate its real behavior. Therefore, this article proposed a new approach based on a recent meta-heuristic algorithm of artificial ecosystem-based optimization (AEO) to identify the optimal parameters of PV cell and module models. Various PV models are considered in this work as single diode (SD), double diode (DD), and triple diode (TD)-based circuits. The analysis is performed on which are R.T.C. France silicon solar cell, FSM-25 PV module, and Canadian-Solar-(CS6P-240P) multi-crystalline solar panel with the aid of experimental data under different operating conditions. Moreover, Lambert form is employed to validate the constructed model. Furthermore, comparative analysis with Harris hawks optimizer (HHO), gray wolf optimizer (GWO), and salp swarm algorithm (SSA) is performed. Additionally, statistical analysis using the Wilcoxon signed rank test is implemented across the three series of experiments for all employed optimizers. The obtained results confirmed the competence of the proposed approach in identifying the PV cell and modules equivalent circuits' parameters.     

On dual-phase-lag magneto-thermo-viscoelasticity theory with memory-dependent derivative

A new mathematical model of generalized magneto-thermo-viscoelasticity theories with memory-dependent derivatives (MDD) of dual-phase-lag heat conduction law is developed. The equations for one-dimensional problems including heat sources are cast into matrix form using the state space and Laplace transform techniques. The resulting formulation is applied to a problem for the whole space with a plane distribution of heat sources. It is also applied to a perfect conducting semi-space problem with a traction-free surface and plane distribution of heat sources located inside the medium. The inversion of the Laplace transforms is carried out using a numerical approach. Numerical results for the temperature, displacement, stress and heat flux distributions as well as the induced magnetic and electric fields are given and illustrated graphically. A comparison is made with the results obtained in the coupled theory. The impacts of the MDD heat transfer parameter and Alfven velocity on a viscoelastic material, for example, poly (methyl methacrylate) (Perspex) are discussed.

     

Bayesian Estimation of Density via Multiple Sequential Inversions of Two-Dimensional Images With Application to Electron Microscopy

We present a new Bayesian methodology to learn the unknown material density of a given sample by inverting its two-dimensional images that are taken with a scanning electron microscope. An image results from a sequence of projections of the convolution of the density function with the unknown microscopy correction function that we also learn from the data; thus, learning of the unknowns demands multiple inversions. We invoke a novel design of experiment, involving imaging at multiple values of the parameter that controls the subsurface depth from which information about the density structure is carried, to result in the image. Real-life material density functions are characterized by high-density contrasts and are highly discontinuous, implying that they exhibit correlation structures that do not vary smoothly. In the absence of training data, modeling such correlation structures of real material density functions is not possible. So we discretize the material sample and treat values of the density function at chosen locations inside it as independent and distribution-free parameters. Resolution of the available image dictates the discretization length of the model; three models pertaining to distinct resolution classes (at micrometer to nanometer scale lengths) are developed. We develop priors on the material density, such that these priors adapt to the sparsity inherent in the density function. The likelihood is defined in terms of the distance between the convolution of the unknown functions and the image data. The posterior probability density of the unknowns given the data is expressed using the developed priors on the density and priors on the microscopy correction function as elicited from the microscopy literature. We achieve posterior samples using an adaptive Metropolis-within-Gibbs inference scheme. The method is applied to learn the material density of a three-dimensional sample of a nano-structure, using real image data. Illustrations on simulated image data of alloy samples are also included.     

Electron Beam Synthesis and Characterization of Poly Vinyl Alcohol/Poly Acrylic Acid Embedded Ni and Ag Nanoparticles

Electron beam irradiation was applied to prepare poly (vinyl alcohol) and poly (acrylic acid) P (PVA/AAc) containing nickel and silver nanoparticles. The prepared P (PVA/AAc)–Ni and P (PVA/AAc)–Ag nanoparticles were characterized by Fourier-transform infrared, UV–Visible spectroscopy, X-ray diffraction (XRD) and transmission electron microscope (TEM). The electrical conductivity and thermal gravimetrical analysis (TGA) have been investigated. Bacterial sensitivity toward nickel and silver nanoparticles was studied. The XRD and TEM confirmed that by increasing the Ni or Ag contents from 10 to 150?mmol in the copolymers, the metal particle size increases from 27.6 to 45.6?nm for Ni and from 14.8 to 37.4?nm for Ag. Meanwhile, the mean size particle increases from 33.02 to 45.05?nm for Ni and from 15.5 to 44.03?nm for Ag. The electrical conductivity of the polymer containing Ag is higher than that of Ni and it increased by increasing the metal content. The TGA studies confirmed that, the thermal stability increase by the introduction of metal into polymeric complex. Bacterial sensitivity to metal nanoparticles was found to vary depending on the microbial species. Disc diffusion studies with P. aeruginosa, E. coli and K. pneumoniae revealed greater effectiveness of the silver nanoparticles compared to the nickel nanoparticles, S. aureus depicted the highest sensitivity to nanoparticles compared to the other strains and was more adversely affected by the nickel nanoparticles.     

Fully developed mixed convection flow of a nanofluid through an inclined channel filled with a porous medium

In this paper, the steady fully developed mixed convection flow of a nanofluid in a channel filled with a porous medium is presented. The walls of the channel are heated by a uniform heat flux and a constant flow rate is considered through the channel. The equations of the problem are made non-dimensional and are observed to depend on the dimensionless parameters, namely the mixed convection parameter λ, the Péclet number Pe, the inclination angle of the channel to the horizontal γ and the nanoparticle volume fraction ?. The effects of these parameters on the fluid and heat transfer characteristics are in detail discussed for three different nanofluids as Cu–water, Al₂O₃–water and TiO₂–water.