The preparation of nanocomposite materials from carbon nanotubes (CNTs) and metal or metal oxide nanoparticles has important implications to the development of advanced catalytic and sensory materials. This paper reports findings of an investigation of the preparation of nanoparticle-coated carbon nanotube composite materials. Our approach involves molecularly mediated assembly of monolayer-capped nanoparticles on multiwalled CNTs via a combination of hydrophobic and hydrogen-bonding interactions between the capping/mediating shell and the CNT surface. The advantage of this route is that it does not require tedious surface modification of CNTs. We have demonstrated its simplicity and effectiveness for assembling alkanethiolate-capped gold nanoparticles of 2-5 nm core sizes onto CNTs with controllable coverage and spatially isolated character. The loading and distribution of the nanoparticles on CNTs depend on the relative concentrations of gold nanoparticles, CNTs, and mediating or linking agents. The composite nanomaterials can be dispersed in organic solvent, and the capping/linking shells can be removed by thermal treatment to produce controllable nanocrystals on the CNT surfaces. The nanocomposite materials are characterized using transmission electron microscopy and Fourier transform infrared spectroscopy techniques. The results will be discussed in terms of developing advanced catalytic and sensory nanomaterials.

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A previous attempt to categorize yeast proteins based on certain attributes yielded only a 55% success rate of correct categorisation using a new type of decision procedure [4]. This paper considers using existing soft computing approaches to improve the categorisation. More specijically, learning algorithms based on neural networks, growing cell systems, a rule development algorithm and genetic algorithms are applied to the yeast data. All of the results are at least as good as the original datu showing that new problems do not necessarily require new algorithms. More interestingly, as a consequence of using diferent algorithms, a consistent failure to achieve high success rates actually indicates features of the data rather than the failings of one or other of the algorithms.

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