Microbial Fuel Cells: Methodology and Technology

Microbial fuel cell (MFC) research is a rapidly evolving field that lacks established terminology and methods for the analysis of system performance. This makes it difficult for researchers to compare devices on an equivalent basis. The construction and analysis of MFCs requires knowledge of different scientific and engineering fields, ranging from microbiology and electrochemistry to materials and environmental engineering. DescribingMFCsystems therefore involves an understanding of these different scientific and engineering principles. In this paper, we provide a review of the different materials and methods used to construct MFCs, techniques used to analyze system performance, and recommendations on what information to include in MFC studies and the most useful ways to present results.

Factors affecting hot spot development in bulk coal and associated gas evolution

There are many physical factors that can affect the self-heating rate of coal. The presence of seam gas has often been referred to as inhibiting coal self-heating due to the limited access of oxidation sites created by the presence of the gas adsorbed on the coal pores. Similarly, the presence of bed moisture in the coal also acts as an inhibitor of oxidation by blocking access of air into the pores. Gas drainage of a coal seam prior to mining removes both gas and moisture from the seam. Bulk coal self-heating tests in a two-metre column on both gassy, as-mined and gas-drained, dried high volatile bituminous coal show that removal of gas and moisture from the coal accelerates the rate of self-heating to thermal runaway from 8.5 days to 4.25 days, from a start temperature of 30°C, with an airflow of 0.25 L/min. The corresponding gas evolution pattern for each of these situations is different. Therefore, it is necessary to take this change in coal condition into consideration when developing a spontaneous combustion management plan.

Effects of potential models in the vapor-liquid equilibria and adsorption of simple gases on graphitized thermal carbon black

In this paper, we investigate the effects of various potential models in the description of vapor–liquid equilibria (VLE) and adsorption of simple gases on highly graphitized thermal carbon black. It is found that some potential models proposed in the literature are not suitable for the description of VLE (saturated gas and liquid densities and the vapor pressure with temperature). Simple gases, such as neon, argon, krypton, xenon, nitrogen, and methane are studied in this paper. To describe the isotherms on graphitized thermal carbon black correctly, the surface mediation damping factor introduced in our recent publication should be used to calculate correctly the fluid–fluid interaction energy between particles close to the surface. It is found that the damping constant for the noble gases family is linearly dependent on the polarizability, suggesting that the electric field of the graphite surface has a direct induction effect on the induced dipole of these molecules. As a result of this polarization by the graphite surface, the fluid–fluid interaction energy is reduced whenever two particles are near the surface. In the case of methane, we found that the damping constant is less than that of a noble gas having the similar polarizability, while in the case of nitrogen the damping factor is much greater and this could most likely be due to the quadrupolar nature of nitrogen.

Flexible synthesis of enantiomerically pure 2,8-dialkyl-1,7-dioxaspiro[5.5]undecanes and 2,7-dialkyl-1,6-dioxaspiro[4.5]decanes from propargylic and homopropargylic alcohols

A new approach to enantiomerically pure 2,8-dialkyl-1,7-dioxaspiro[5.5]undecanes and 2,7-dialkyl-1,6-dioxaspiro [4.5] decanes is described and utilizes enantiomerically pure homopropargylic alcohols obtained from lithium acetylide opening of enantiomerically pure epoxides, which are, in turn, acquired by hydrolytic kinetic resolution of the corresponding racemic epoxides. Alkyne carboxylation and conversion to the Weinreb amide may be followed by triple-bond manipulation prior to reaction with a second alkynyllithium derived from a homo- or propargylic alcohol. In this way, the two ring components of the spiroacetal are individually constructed, with deprotection and cyclization affording the spiroacetal. The procedure is illustrated by acquisition of (2S,5R,7S) and (2R,5R,7S)-2-n-butyl-7-methyl-1,6-dioxaspiro[4.5]-decanes (1), (2S,6R,8S)-2-methyl-8-n-pentyl-1,7-dioxaspiro[5.5]undecane (2), and (2S,6R,8S)-2-methyl-8-n-propyl-1,7-dioxaspiro[5.5]undecane (3). The widely distributed insect component, (2S,6R,8S)-2,8-dimethyl-1,7-dioxaspiro[5.5]undecane (4), was acquired by linking two identical alkyne precursors via ethyl formate. In addition, [H-2(4)]-regioisomers, 10,10,11,11-[H-2(4)] and 4,4,5,5-[H-2(4)] of 3 and 4,4,5,5-[H-2(4)]-4, were acquired by triple-bond deuteration, using deuterium gas and Wilkinson's catalyst. This alkyne-based approach is, in principle, applicable to more complex spiroacetal systems not only by use of more elaborate alkynes but also by triple-bond functionalization during the general sequence.

Electricity market price spike forecast with data mining techniques

Electricity market price forecast is a changeling yet very important task for electricity market managers and participants. Due to the complexity and uncertainties in the power grid, electricity prices are highly volatile and normally carry with spikes. which may be (ens or even hundreds of times higher than the normal price. Such electricity spikes are very difficult to be predicted. So far. most of the research on electricity price forecast is based on the normal range electricity prices. This paper proposes a data mining based electricity price forecast framework, which can predict the normal price as well as the price spikes. The normal price can be, predicted by a previously proposed wavelet and neural network based forecast model, while the spikes are forecasted based on a data mining approach. This paper focuses on the spike prediction and explores the reasons for price spikes based on the measurement of a proposed composite supply-demand balance index (SDI) and relative demand index (RDI). These indices are able to reflect the relationship among electricity demand, electricity supply and electricity reserve capacity. The proposed model is based on a mining database including market clearing price, trading hour. electricity), demand, electricity supply and reserve. Bayesian classification and similarity searching techniques are used to mine the database to find out the internal relationships between electricity price spikes and these proposed. The mining results are used to form the price spike forecast model. This proposed model is able to generate forecasted price spike, level of spike and associated forecast confidence level. The model is tested with the Queensland electricity market data with promising results. Crown Copyright (C) 2004 Published by Elsevier B.V. All rights reserved.