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Taniguchi, M.

Publications and source records attributed to Taniguchi, M..

Hybridization of Bogoliubov Quasiparticles between Adjacent CuO 2 Layers in the Triple-Layer Cuprate Bi 2 Sr 2 Ca 2 Cu 3 O 10 + δ Studied by Angle-Resolved Photoemission Spectroscopy

Hybridization of Bogoliubov quasiparticles (BQPs) between the CuO 2 layers in the triple-layer cuprate high-temperature superconductor Bi 2 Sr 2 Cu 2 Cu 3 O 10+δ is studied by angle-resolved photoemission spectroscopy (ARPES). In the superconducting state, an anticrossing gap opens between the outer- and inner-BQP bands, which we attribute primarily to interlayer single-particle hopping with possible contributions from interlayer Cooper pairing. We find that the d-wave superconducting gap of both BQP bands smoothly develops with momentum without an abrupt jump in contrast to a previous ARPES study. Hybridization between the BQPs also gradually increases in going from the off nodal to the antinodal region, which is explained by the momentum dependence of the interlayer single-particle hopping. As possible mechanisms for the enhancement of the superconducting transition temperature, the hybridization between the BQPs as well as the combination of phonon modes of the triple CuO 2 layers and spin fluctuations represented by a four-well model are discussed.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Development of an advanced failure detection algorithm for the SSME

Since ground testing of the Space Shuttle Main Engine began in 1975, a multifaceted detection/shutdown system has been used to detect engine anomalies and initiate shutdown. In spite of this system, 27 major incidents have occurred since testing began. Although a small percentage of over 1200 successful hot fire tests, such incidents can result in significant cost and schedule impacts. Therefore, an advanced failure detection algorithm has been developed which consists of three detection approaches designed for a failure characteristic that has been previously documented. Simulations using sensor data from five incident tests show the algorithm detecting an anomally and signalling shutdown prior to the redline cutoff time. The foundation elements of the algorithm and its functioning are described.

Norman, A.↗