Aliphatic amine based nanocarbons for the absorption of carbon dioxide

A composition of matter, and method to make same, for a nano-based material including a nanocarbon support to which is attached an aliphatic amine. In particular, the composition of matter is an aliphatic amine-nanocarbon material that includes a nanocarbon (NC) support, such as C60, nano-graphite, graphene, nanocarbon ribbons, graphite intercalation compounds, graphite oxide, nano-coal, nanohorns, and combinations thereof, and further includes an aliphatic amine, such as polyethyleneimine (PEI)

Understanding the “Activation” of the Nanocluster [HxPMo12O40⊂H4Mo72Fe30(O2CMe)15O254(H2O)98-y(EtOH)y] for Low Temperature Growth of Carbon Nanotubes

The molecular nanocluster HxPMo12O40⊂H4Mo72Fe30(O2CMe)15O254(H2O)98-y(EtOH)y (FeMoC), was the first molecular catalyst precursor (pro-catalyst) that promised controlled growth of carbon nanotubes (CNTs); however, temperatures in excess of ~ 900 °C or the addition of excess iron were required as catalyst promoters for CNT growth. To understand these disappointing results the “activation” of FeMoC for CNT growth was studied by systematic investigation of H2 gas concentration and growth temperature. The pathway for “activation” of FeMoC occurs through the sufficient reduction of both metal oxide components in the pro-catalyst. By ensuring pro-catalyst reduction prior to introduction of growth gases, we demonstrate for the first time, growth of CNTs at temperatures as low as 600 °C without the use of catalyst promoters using the single molecular precursor, FeMoC. To understand the role of catalyst promoters used in prior work, thermogravimetric analysis experiments were performed. The addition of an iron catalyst promoter is observed to play two key roles in the “activation” of FeMoC: (1) to replenish sublimated metal atoms, and (2) to reduce the reduction temperature required for reduction of FeMoC into an “active” catalyst. These results caution the conditions employed in many earlier studies for CNT growth, and create new possibilities for molecular pro-catalysts

Minority carrier device comprising a passivating layer including a Group 13 element and a chalcogenide component

A minority carrier device includes at least one junction of at least two dissimilar materials, at least one of which is a semiconductor, and a passivating layer on at least one surface of the device. The passivating layer includes a Group 13 element and a chalcogenide component. Embodiments of the minority carrier device include, for example, laser diodes, light emitting diodes, heterojunction bipolar transistors, and solar cells

Approximation and learning by greedy algorithms

We consider the problem of approximating a given element $f$ from a Hilbert space $\mathcal{H}$ by means of greedy algorithms and the application of such procedures to the regression problem in statistical learning theory. We improve on the existing theory of convergence rates for both the orthogonal greedy algorithm and the relaxed greedy algorithm, as well as for the forward stepwise projection algorithm. For all these algorithms, we prove convergence results for a variety of function classes and not simply those that are related to the convex hull of the dictionary. We then show how these bounds for convergence rates lead to a new theory for the performance of greedy algorithms in learning. In particular, we build upon the results in [IEEE Trans. Inform. Theory 42 (1996) 2118--2132] to construct learning algorithms based on greedy approximations which are universally consistent and provide provable convergence rates for large classes of functions. The use of greedy algorithms in the context of learning is very appealing since it greatly reduces the computational burden when compared with standard model selection using general dictionaries.Comment: Published in at http://dx.doi.org/10.1214/009053607000000631 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org