Cobalt‐filled carbon nanotube composites (see Figure) with a high metal‐filling ratio can be prepared by a simple and effective method. The length, diameter, and structure of the metal‐filled nanotubes and the wall thickness of the plain carbon nanotubes can be easily controlled by the template used and synthesis conditions. Magnetic measurements reveal that the array of cobalt‐filled carbon nanotubes exhibits enhanced coercivities in comparision to that of bulk Co.

An array of single crystal CdS nanowires has been fabricated in the pores of an alumina membrane by sol–gel synthesis. The CdS crystals have a hexagonal structure, which is verified by electron diffraction. As CdS is an important semiconducting material this synthesis may prove very useful for the fabrication of nanosized semiconducting structures. The Figure shows a scanning electron microscopy image of the nanowires.

Zirconia nanotubules, filled with crystalline cobalt rods (see Figure), were prepared in a reaction sequence of sol–gel crystallization of ZrO2 from zirconyl chloride octahydrate around a template of commercial porous anodic alumina, followed by electrodeposition of cobalt from CoSO4 solution and template removal with 6 N NaOH. The composite shows uniaxial magnetic anisotropy and a coercivity exceeding that of bulk cobalt.

Char oxidation is often modeled using an mth order intrinsic reaction rate in conjunction with an effectiveness factor (η) to account for intraparticle diffusion of gas species. This approach involves the use of a general modulus (M T ) and using the first-order curve of η vs M T . This method was originally referred to as the general asymptotic solution. It has been suggested that a simple Langmuir rate equation is more suitable for modeling the effects of pressure on char reactivity. Therefore, several methods of developing general moduli for the Langmuir rate expression are shown. The general asymptotic solution is most accurate as M T approaches the limits of zero and infinity. However, in the intermediate range of M T (0.2 < M T < 5), the general asymptotic solution exhibits errors of up to -17% error in spherical coordinates and -24% error in Cartesian coordinates. A correction function was constructed to improve the accuracy of predictions in the intermediate range of general modulus for both the mth-order and the Langmuir rate equations. The general asymptotic solution, combined with this correction function, is able to predict the effectiveness factor for all mth-order (0 <) m <) 1) and Langmuir rate equations within (2%. The observed reaction order of char oxidation has been reported to change as a function of temperature, with limits of 0 and 1. A theory has been developed to quantitatively explain and predict this change of reaction order based on the Langmuir rate equation in conjunction with the effectiveness factor approach.

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The global nth order rate equation has been criticized for lack of theoretical basis and has been shown to be inadequate for modeling char oxidation rates as a function of total gas pressure. The simple Langmuir rate equation is believed to have more potential for modeling high pressure char oxidation. The intrinsic Langmuir rate equation is applied to graphite flake oxidation data and agrees well with reaction rates at three temperatures over the entire range of oxygen pressure (1-64 atm). It also explains the change of reaction order with temperature.In this work, the intrinsic Langmuir rate equation is combined with (1) an effectiveness factor to account for pore diffusion effects and (2) a random pore structure model to calculate effective diffusivity.The resulting model is able to predict the reaction rates of large (ca. 8 mm) coal char particles as a function of gas velocity, total pressure, oxygen partial pressure, oxygen mole fraction, initial particle size, and gas temperature. This approach is also able to correlate the particle burnouts of pulverized (70 lm) coal char particles in a drop tube reactor as a function of total pressure, oxygen mole fraction, gas and wall temperatures, and residence time. The ability of the model to correlate data over wide range of temperature and pressure is promising.

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