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Results for “combinatorial thin films”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
NASA NTRS2006

Advanced Catalysts for Fuel Cells

This viewgraph presentation reviews the development of catalyst for Fuel Cells. The objectives of the project are to reduce the cost of stack components and reduce the amount of precious metal used in fuel cell construction. A rapid combinatorial screening technique based on multi-electrode thin film array has been developed and validated for identifying catalysts for oxygen reduction; focus shifted from methanol oxidation in FY05 to oxygen reduction in FY06. Multi-electrode arrays of thin film catalysts of Pt-Ni and Pt-Ni-Zr have been deposited. Pt-Ni and have been characterized electrochemically and structurally. Pt-Ni-Zr and Pt-Ni films show higher current density and onset potential compared to Pt. Electrocatalytic activity and onset potential are found to be strong function of the lattice constant. Thin film Pt(59)Ni(39)Zr(2) can provide 10 times the current density of thin film Pt. Thin film Pt(59)Ni(39)Zr(2) also shows 65mV higher onset potential than Pt.

fuel cells↗
NASA NTRS1990

Programmable synaptic devices for electronic neural nets

The architecture, design, and operational characteristics of custom VLSI and thin film synaptic devices are described. The devices include CMOS-based synaptic chips containing 1024 reprogrammable synapses with a 6-bit dynamic range, and nonvolatile, write-once, binary synaptic arrays based on memory switching in hydrogenated amorphous silicon films. Their suitability for embodiment of fully parallel and analog neural hardware is discussed. Specifically, a neural network solution to an assignment problem of combinatorial global optimization, implemented in fully parallel hardware using the synaptic chips, is described. The network's ability to provide optimal and near optimal solutions over a time scale of few neuron time constants has been demonstrated and suggests a speedup improvement of several orders of magnitude over conventional search methods.

Moopenn, A.↗