Articles you may be interested inBinding energy and mechanical stability of single-and multi-walled carbon nanotube serpentines

Motivated by the work of Erdogmus and Principe, we use the error (h, φ)-entropy as the supervised adaptation criterion. Several properties of the (h, φ)-entropy criterion and the connections with traditional error criteria are investigated. By a kernel estimate approach, we obtain the nonparametric estimator of the instantaneous (h, φ)-entropy. Then, we develop the general stochastic information gradient algorithm, and derive the approximate upper bound for the step size in the adaptive linear neuron training. Moreover, the (h, φ) pair are optimized to improve the performance of the proposed algorithm. For the finite impulse response identification with white Gaussian input and noise, the exact optimum φ function is derived. Finally, simulation experiments verify the results and demonstrate the noticeable performance improvement that may be achieved by the optimum (h, φ)-entropy criterion.

Using Raman spectroscopy, we demonstrate that the anisotropic interaction between single‐walled carbon nanotubes (SWNTs) and poly(methyl methacrylate) (PMMA) causes significant changes in the electronic properties of their composites. Two different procedures were used to prepare the composites: melt blending and in‐situ UV polymerization. Resonant Raman studies relate the electronic density of states (DOS) of the SWNTs to the corresponding vibration symmetry changes of both the PMMA and the SWNTs. Our results show that, in the melt‐blended sample, the SWNTs—originally semiconducting—became predominantly metallic. The changes in the electronic properties were also confirmed by dielectric constant measurements. We propose that the anisotropic interaction between PMMA and SWNTs in the melt‐blended composite is the dominant reason for the observed electronic character change.

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