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Wall, James

Publications and source records attributed to Wall, James.

Final Report on Characterization of Irradiated Sensors and Coupling Adhesive Bonds

This report summarizes characterization via scanning/transmission electron microscopy of the microstructures of the unirradiated and irradiated piezoelectric ultrasonic sensor/aluminum substrate assemblies using four commercially available inorganic coupling adhesives to bond two types of piezoelectric crystals to the substrates. The sample assemblies were irradiated in the PULSTAR reactor at NC State University and ultrasonic data was collected in-situ. ORNL LAMDA Laboratory capabilities were utilized to perform pre- and post-irradiation examination of the sensor assemblies. This document summarizes the PIE performed at the ORNL LAMDA laboratory. The results of the PIE described in this report are consistent with the ultrasonic data collected during irradiation – in particular, high temperature epoxy adhesive seemed to provide the best coupling as compared to the refractory ceramic adhesives. It was determined that the quality of the sensor-adhesive-substrates governed the ultrasonic performance of the sensors. It was also apparent that irradiation did not significantly affect bond quality, which is also supported by the ultrasonic data collected during irradiation. A more comprehensive DOE NSUF Final Report including details of materials selection, sample fabrication, initial ultrasonic testing, irradiation and in-situ ultrasonic testing, positron annihilation lifetime spectroscopy and doppler broadening spectroscopy performed at EPRI and NC State University will be published at the conclusion of the project.

36 MATERIALS SCIENCE↗

On-board Neural Processor Design for an Intelligent Multi-sensor Microspacecraft

A compact VLSI neural processor based on the Optimization Cellular Neural Network (OCNN)has been under development to provide a wide range of support for an intelligent remote sensing microspacecraft which requires both high bandwidth communication and high-performance computing for on-board data analysis, thematic data reduction, synergy of multiple types of sensors, and other smart-sensor functions. The OCNN architecture is a programmable multi-dimensional array of neurons which are locally connected with their local neurons. The OCNN operation theory, architecture, design and implementation, prototype chip, and system applications have been investigated in detail and presented in this paper.

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