Methanol steam reforming in a compact plate-fin reformer for fuel-cell systems

A compact plate-fin reformer (PFR) consisting of closely spaced plate-fins, in which endothermic and exothermic reactions take place in alternate chambers, has been studied. In the PFR, which was based on a plate-fin heat exchanger, catalytic combustion of the reforming gas, as a simulation of the fuel cell anode off gas (AOG), supplied the necessary heat for the reforming reaction. One reforming chamber, which was for hydrogen production, was integrated with two vaporization chambers and two combustion chambers to constitute a single unit of PFR. The PFR is very compact, easy to be placed and scaled up. The effect of the ratio of H2O/CH3OH on the performance of the PFR has been investigated, and temperature distributions in different chambers were studied. Besides, the stationary behavior of the PFR was also investigated. Heat transfer of the reformer was enhanced by internal plate-fins as well as by external catalytic combustion, which offer both high methanol conversion ratio and low CO concentration. In addition, the fully integrated reformer exhibited good test stability. Based on the PFR, a scale-up reformer was designed and operated continuously for 1000 h, with high methanol conversion ratio and low CO concentration. (c) 2004 International Association for Hydrogen Energy. Published by Elsevier Ltd. All rights reserved.

Correcting the systematic error of the density functional theory calculation: the alternate combination approach of genetic algorithm and neural network

The alternate combinational approach of genetic algorithm and neural network (AGANN) has been presented to correct the systematic error of the density functional theory (DFT) calculation. It treats the DFT as a black box and models the error through external statistical information. As a demonstration, the AGANN method has been applied in the correction of the lattice energies from the DFT calculation for 72 metal halides and hydrides. Through the AGANN correction, the mean absolute value of the relative errors of the calculated lattice energies to the experimental values decreases from 4.93% to 1.20% in the testing set. For comparison, the neural network approach reduces the mean value to 2.56%. And for the common combinational approach of genetic algorithm and neural network, the value drops to 2.15%. The multiple linear regression method almost has no correction effect here.

Electrochemiluminescence detection of NADH and ethanol based on partial sulfonation of sol-gel network with gold nanoparticles

We developed a stable, sensitive electrochemiluminescence (ECL) biosensor based on the synthesis of a new sol-gel material with the ion-exchange capacity sol-gel to coimmobilize the Ru(bpy)(3)(2+) and enzyme. The partial sulfonated (3-mercaptopropyl)-trimethoxysilane sol-gel (PSSG) film acted as both an ion exchanger for the immobilization of Ru(bpy)(3)(2+) and a matrix to immobilize gold nanoparticles (AuNPs). The AuNPs/PSSG/Ru(bpy)(3)(2+) film modified electrode allowed sensitive the ECL detection of NADH as low as 1 nM. Such an ability of AuNPs/PSSG/Ru(bpy)(3)(2+) film to promote the electron transfer between Ru(bpy)(3)(2+) and the electrode suggested a new, promising biocompatible platform for the development of dehydrogenase-based ECL biosensors. With alcohol dehydrogenase (ADH) as a model, we then constructed an ethanol biosensor, which had a linear range of 5 mu M to 5.2 mM with a detection limit of 12 nM.

Radiation crosslinking of biodegradable poly(butylene succinate) and its heat distortion behavior

Poly(butylene succinate), (PBS1) was irradiated with Co-60-gamma radiation at various temperatures. The gel fraction of PBS I irradiated at molten state (100 degreesC) is higher than that of the samples irradiated at lower temperatures with the same dose. Two-step irradiation ( irradiation at room temperature and then irradiation at 100 degreesC) yielded the highest gel content as compared with other treatment conditions. It is due to the network structure formed by preirradiation at room temperature and further irradiation at molten state reduce degradation of PBS1. PBS1 prepared by the two-step irradiation was improved in heat distortion resistance because of its higher gel content. Unirradiated PBS1 sheets broke immediately at 110 degreesC. On the other hand, for samples (gel fraction 50%) irradiated by asing the two-step method, they did not break even at 130 degreesC for 200 min.

Neural network retrieval of ocean surface parameters from SSM/I data

A new algorithm based on the multiparameter neural network is proposed to retrieve wind speed (WS), sea surface temperature (SST), sea surface air temperature, and relative humidity ( RH) simultaneously over the global oceans from Special Sensor Microwave Imager (SSM/I) observations. The retrieved geophysical parameters are used to estimate the surface latent heat flux and sensible heat flux using a bulk method over the global oceans. The neural network is trained and validated with the matchups of SSM/I overpasses and National Data Buoy Center buoys under both clear and cloudy weather conditions. In addition, the data acquired by the 85.5-GHz channels of SSM/I are used as the input variables of the neural network to improve its performance. The root-mean-square (rms) errors between the estimated WS, SST, sea surface air temperature, and RH from SSM/I observations and the buoy measurements are 1.48 m s(-1), 1.54 degrees C, 1.47 degrees C, and 7.85, respectively. The rms errors between the estimated latent and sensible heat fluxes from SSM/I observations and the Xisha Island ( in the South China Sea) measurements are 3.21 and 30.54 W m(-2), whereas those between the SSM/ I estimates and the buoy data are 4.9 and 37.85 W m(-2), respectively. Both of these errors ( those for WS, SST, and sea surface air temperature, in particular) are smaller than those by previous retrieval algorithms of SSM/ I observations over the global oceans. Unlike previous methods, the present algorithm is capable of producing near-real-time estimates of surface latent and sensible heat fluxes for the global oceans from SSM/I data.