Abstract4,4'-(Hexafluoroisopropylidene) diphthalic anhydride 2,3,5,6-tetramethyl-1,4-phenylenediamine (6FDA-TeMPD) polyimide macroinitiator was synthesized and reacted with poly(methyl methacrylate) (PMMA) to form an ABA-type triblock copolymer by atom transfer radical polymerization. The effect of the ABA-type triblock copolymer structure on solid, thermal, optical and gas transport properties was systematically investigated and compared with the physical blend polymer. The blend polymer was cloudy, whereas the triblock copolymer was colorless and transparent. The PMMA component decomposition temperature for the triblock copolymer slightly shifted to higher temperature, while its gas barrier property was higher than the blend polymer. The refractive index and the gas permeability decreased while maintaining the heat resistance by a high nanoscale distribution of both polymer components. The 6FDA-TeMPD/PMMA ABA-type triblock copolymer can be described as a polymer material with high heat resistance, high gas barrier property and low refractive index amongst existing polymers.
Inverted transparent organic light-emitting diodes (i-TOLEDs) were fabricated using thermal evaporation and spray methods. Only oxide compounds were used for the electron injection layer (EIL) of the diodes, and no alkali metals were included. Furthermore, these i-TOLEDs were fabricated with a nanoscale metal/dielectric multilayered transparent conductive film, which served as the anode.
Takehisa SAITOH, Hiroto SUNAHARA, Masato ICHIKAWA, Tadashi FUKUMOTO, Hajime MASE, and Hajime ISHIDA This study aims to develop a highly accurate method for an estimation of hydraulic characteristics around an artificial reef by using an artificial neural network, instead of a conventional method using a regression analysis. The wave trapping artificial reef is chosen as the structure of an artificial reef, and transmission coefficient and wave setup behind the reef are focused as hydraulic characteristics. For the training of the artificial neural network, the Levenberg-Marquardt method with Bayesian regulation is employed. Predictions of transmission coefficient and wave setup from the trained network agreed well with the experimental results comparing with predicted ones from the method using a regression analysis.
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