Plasma stabilisation of metallic nanoparticles on silicon for the growth of carbon nanotubes

Ammonia (NH 3) plasma pretreatment is used to form and temporarily reduce the mobility of Ni, Co, or Fe nanoparticles on boron-doped mono- and poly-crystalline silicon. X-ray photoemission spectroscopy proves that NH 3 plasma nitrides the Si supports during nanoparticle formation which prevents excessive nanoparticle sintering/diffusion into the bulk of Si during carbon nanotube growth by chemical vapour deposition. The nitridation of Si thus leads to nanotube vertical alignment and the growth of nanotube forests by root growth mechanism. © 2012 American Institute of Physics.

On the relation of slow feature analysis and Laplacian eigenmaps

The past decade has seen a rise of interest in Laplacian eigenmaps (LEMs) for nonlinear dimensionality reduction. LEMs have been used in spectral clustering, in semisupervised learning, and for providing efficient state representations for reinforcement learning. Here, we show that LEMs are closely related to slow feature analysis (SFA), a biologically inspired, unsupervised learning algorithm originally designed for learning invariant visual representations. We show that SFA can be interpreted as a function approximation of LEMs, where the topological neighborhoods required for LEMs are implicitly defined by the temporal structure of the data. Based on this relation, we propose a generalization of SFA to arbitrary neighborhood relations and demonstrate its applicability for spectral clustering. Finally, we review previous work with the goal of providing a unifying view on SFA and LEMs. © 2011 Massachusetts Institute of Technology.