Monitoring microbial sulfate reduction in porous media using multi-purpose electrodes

There is growing interest in the application of electrode-based measurements for monitoring microbial processes in the Earth using biogeophysical methods. In this study, reactive electrode measurements were combined to electrical geophysical measurements during microbial sulfate reduction occurring in a column of silica beads saturated with natural river water. Electrodic potential (EP), self potential (SP) and complex conductivity signals were recorded using a dual electrode design (Ag/AgCl metal as sensing/EP electrode, Ag/AgCl metal in KCl gel as reference/SP electrode). Open-circuit potentials, representing the tendency for electrochemical reactions to occur on the electrode surfaces, were recorded between sensing/EP electrode and reference/SP electrode and showed significant spatiotemporal variability associated with microbial activity. The dual electrode design isolates the microbial driven sulfide reactions to the sensing electrode and permits removal of any SP signal from the EP measurement. Based on the known sensitivity of a Ag electrode to dissolved sulfide, we interpret EP signals exceeding 550 mV recorded in this experiment in terms of bisulfide (HS-) concentration near multiple sensing electrodes. Complex conductivity measurements capture an imaginary conductivity (s?) signal interpreted as the response of microbial growth and biomass formation in the column. Our results suggest that the implementation of multipurpose electrodes, combining reactive measurements with electrical geophysical measurements, could improve efforts to monitor microbial processes in the Earth using electrodes.

Clean preparation of nanoparticulate metals in porous supports: a supercritical route

Here we present the synthesis of nanometre sized silver particles which have been trapped within porous substrates; poly( styrene-divinylbenzene) beads and silica aerogels. This is the first time that supercritical carbon dioxide has been used to impregnate such porous materials with silver coordination complexes. In this paper we demonstrate that control over the resultant nanoparticles with respect to size, loading and distribution in the support material has been achieved by simple choice of the precursor complex. The solubility of the precursor complexes in the supercritical solvent is shown to be one of the key parameters in determining the size of the nanoparticles, their distribution and their homogeneity within the support matrix. Moreover, we demonstrate that the same methodology can be applied to two very different substrate materials. In the particular case of aerogels, conventional organic solvents could not be used to prepare nanoparticles because the surface tension of the solvent would lead to fracturing of the aerogel structure.

Short, medium and long term load forecasting model and virtual load forecaster based on radial basis neural networks

Artificial neural networks (ANNs) can be easily applied to short-term load forecasting (STLF) models for electric power distribution applications. However, they are not typically used in medium and long term load forecasting (MLTLF) electric power models because of the difficulties associated with collecting and processing the necessary data. Virtual instrument (VI) techniques can be applied to electric power load forecasting but this is rarely reported in the literature. In this paper, we investigate the modelling and design of a VI for short, medium and long term load forecasting using ANNs. Three ANN models were built for STLF of electric power. These networks were trained using historical load data and also considering weather data which is known to have a significant affect of the use of electric power (such as wind speed, precipitation, atmospheric pressure, temperature and humidity). In order to do this a V-shape temperature processing model is proposed. With regards MLTLF, a model was developed using radial basis function neural networks (RBFNN). Results indicate that the forecasting model based on the RBFNN has a high accuracy and stability. Finally, a virtual load forecaster which integrates the VI and the RBFNN is presented.

Two-dimensional hygrothermal transfer in porous building materials

A two-dimensional mathematical model for evaluating the simultaneous heat and moisture migration in porous building materials was proposed. Vapor content and temperature were chosen as the principal driving potentials. The numerical solution was based on the control volume finite difference technique with fully implicit scheme in time. Two validation experiments were developed in this study. The evolution of transient moisture distributions in both one-dimensional and two-dimensional cases was measured. A comparison between experimental results and those obtained by the numerical model proves that they are fully consistent with each other. The model can be easily integrated into a whole building heat, air and moisture transfer model. Another main advantage of the present numerical method lies in the fact that the required moisture transport properties are comparatively simple and easy to determine.