Nanotechnology for aerospace: potential transitions from university research

Forrest J. Agee

doi:10.1117/12.776736

16 April 2008 Nanotechnology for aerospace: potential transitions from university research

Forrest J. Agee

Proceedings Volume 6979, Independent Component Analyses, Wavelets, Unsupervised Nano-Biomimetic Sensors, and Neural Networks VI; 69790E (2008) https://doi.org/10.1117/12.776736
Event: SPIE Defense and Security Symposium, 2008, Orlando, Florida, United States

Abstract

Nanotechnology is expected to provide the fundamental basis of the next two generations of products and processes. Impacts for applications are already being felt in many fields, and there is interest especially in the aerospace industry, where performance is a major driver of decisions for applications. Four areas are receiving special emphasis in a program aimed at the Air Force's strategic focus on materials. The emphasis includes adaptive coatings and surface engineering, nanoenergetics, electromagnetic sensors, and power generation and storage. Seven universities in Texas have initiated the CONTACT program of focused research including nine projects in the first year, with plans for expansion in subsequent years. This paper discusses the focus, progress, and plans for the second year and opportunities for industry input to the scope and content of the research. A new model for the creation and guidance of research programs for industry is presented. The new approach includes interaction with the aerospace industry and the Air Force that provides a focus for the research. Results to date for the new method and for the research are presented. A discussion of nanoengineering technology transition into the aerospace industry highlights the mechanisms for enhancing the process and for dealing with intellectual property.

Citation Download Citation

Forrest J. Agee "Nanotechnology for aerospace: potential transitions from university research", Proc. SPIE 6979, Independent Component Analyses, Wavelets, Unsupervised Nano-Biomimetic Sensors, and Neural Networks VI, 69790E (16 April 2008); https://doi.org/10.1117/12.776736

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