Mass transfer characteristics of two-aqueous-phase liquid-liquid mixtures

Mass transfer rates were studied using the falling drop method. Cibacron Blue 3 GA dye was the transferring solute from the salt phase to the PEG phase. Measurements were undertaken for several concentrations of the dye and the phase-forming solutes and with a range of different drop sizes, e.g. 2.8, 3.0 and 3.7 mm. The dye was observed to be present in the salt phase as finely dispersed solids but a model confirmed that the mass transfer process could still be described by an equation based upon the Whitman two-film model. The overall mass transfer coefficient increased with increasing concentration of the dye. The apparent mass transfer coefficient ranged from 1 x 10-5 to 2 x 10 -4 m/s. Further experiments suggested that mass transfer was enhanced at high concentration by several mechanisms. The dye was found to change the equilibrium composition of the two phases, leading to transfer of salt between the drop and continuous phases. It also lowered the interfacial tension (i.e. from 1.43 x 10-4 N/m for 0.01% w/w dye concentration to 1.07 x 10-4 N/m for 0.2% w/w dye concentration) between the two phases, which could have caused interfacial instabilities (Marangoni effects). The largest drops were deformable, which resulted in a significant increase in the mass transfer rate. Drop size distribution and Sauter mean drop diameter were studied on-line in a 1 litre agitated vessel using a laser diffraction technique. The effects of phase concentration, dispersed phase hold-up and impeller speed were investigated for the salt-PEG system. An increase in agitation speed in the range 300 rpm to 1000 rpm caused a decrease in mean drop diameter, e.g. from 50 m to 15 m. A characteristic bimodal drop size distribution was established within a very short time. An increase in agitation rate caused a shift of the larger drop size peak to a smaller size.

Efficient THz radiation from a nanocrystalline silicon-based multi-layer photomixer

In this paper we propose a novel type of multiple-layer photomixer based on amorphous/nano-crystalline-Si. Such a device implies that it could be possible to enhance the conversion efficiency from optical power to THz emission by increasing the absorption length and by reducing the device overheating through the use of substrates with higher thermal conductivity compared to GaAs. Our calculations show that the output power from a two-layer Si-based photomixer is at least ten times higher than that from conventional LT-GaAs photomixers at 1 THz.

Towards understanding IT value co-creation in crowdsourcing:the multiple stakeholders' perspective research-in-progress paper

Contemporary business environment involves IT being invested and shared by multiple stakeholders in collaborative, platform-based, and relational arrangements where the objective is to co-create value. Traditional IT enabled business value therefore has been extended towards IT value co-creation that involves multiple stakeholders. In this paper, we present a conceptual development of IT-based value co-creation in the context of online crowdsourcing. Based on the existing literature, we have distinguished multiple crowdsourcing types (models) by analyzing attributes of crowd, the roles of the client, the platform and the crowd that act as key stakeholders in the value co-creation process, and describe the major interactions between the main stakeholders. Our conceptual development is suggesting different combinations of value co-creation layers to be evident in different crowdsourcing models.

Dynamics of on-line gradient descent learning for multilayer neural networks

We consider the problem of on-line gradient descent learning for general two-layer neural networks. An analytic solution is presented and used to investigate the role of the learning rate in controlling the evolution and convergence of the learning process.

On-line learning in multilayer neural networks

We present an analytic solution to the problem of on-line gradient-descent learning for two-layer neural networks with an arbitrary number of hidden units in both teacher and student networks. The technique, demonstrated here for the case of adaptive input-to-hidden weights, becomes exact as the dimensionality of the input space increases.

Adaptive back-propagation in on-line learning of multilayer networks

An adaptive back-propagation algorithm is studied and compared with gradient descent (standard back-propagation) for on-line learning in two-layer neural networks with an arbitrary number of hidden units. Within a statistical mechanics framework, both numerical studies and a rigorous analysis show that the adaptive back-propagation method results in faster training by breaking the symmetry between hidden units more efficiently and by providing faster convergence to optimal generalization than gradient descent.