AlGaN epitaxial lateral overgrowth on Ti-evaporated GaN/sapphire substrate

AlGaN growth using epitaxial lateral overgrowth (ELO) by metalorganic chemical vapor deposition on striped Ti, evaporated GaN on sapphire, has been investigated. AlGaN/AlN films growth on GaN/AlGaN superlattices (SLs) structure on the Ti masks, with various SLs growth temperature (1030, 1060 and 1090 °C) were grown. With increasing the growth temperature, AlGaN surface became flat. The AlGaN film had a cathodoluminescence peak around 345 nm. However, in secondary ion mass spectrometry (SIMS) measurement, Ti signal was detected on the top of AlGaN surface when GaN/AlGaN SLs was grown on Ti striped masks. By inserting the AlN blocking layer on SLs, Ti diffusion was stopped at the AlN layer, and the AlGaN crystalline quality was improved.     

FLUORESCENCE QUENCHING OF 5,10,15,20-TETRA(p-TOLYL)PORPHINE and ITS ZINC COMPLEX BY QUINONES. CHARGE-TRANSFER INTERACTION and TRANSIENT EFFECT

The fluorescent states of tetratolylporphyrin and its zinc complex in acetone were quenched by various kinds of quinones. The transient effect for the collisional quenching caused the upward deviation of the plots of the inversed fluorescence intensities of the porphyrins vs the quinone concentrations. A charge-transfer interaction between the porphyrins in the singlet excited states and the quinones was suggested from the dependence of the quenching rate constants on the electron affinities of the quinones.     

Theoretical investigation of polarization control in ultraviolet wavelength region using eigenmode within subwavelength grating

Optical Review - The optical characteristics of a subwavelength grating (SWG) were examined as a means of achieving ultraviolet (UV) polarization control based on the interaction between a light...     

Lipophilic arsenic compound(s) in the liver of a tiger shark (Galeocerdo cuvier)

Homogenized aliquots (100 g) of the liver (8.4 kg, 5 m?g As g^?1) of a tiger shark (Galeocerdo cuvier) were extracted with chloroform/methanol, and the extracts purified by countercurrent extraction (hexane/87% ethanol), silica gel column chromatography (chloroform/methanol mixtures as mobile phases), and silica gel thin-layer chromatography (chloroform/methanol/acetic acid). The purified samples (24 mg arsenic g^?1) gave no ³¹P NMR signal, but gave ¹H and ¹³C NMR signals with similarities to those of dipalmitoylphosphatidic acid and salad on and also signals indicative of the presence of methylated arsenic compounds. The sample could contain a diacyl glyceride with a methylated arsenic group.     

Constructing catalyst knowledge networks from catalyst big data in oxidative coupling of methane for designing catalysts

Designing high performance catalysts for the oxidative coupling of methane (OCM) reaction is often hindered by inconsistent catalyst data, which often leads to difficulties in extracting information such as combinatorial effects of elements upon catalyst performance as well as difficulties in reaching yields beyond a particular threshold. In order to investigate C₂ yields more systematically, high throughput experiments are conducted in an effort to mass-produce catalyst-related data in a way that provides more consistency and structure. Graph theory is applied in order to visualize underlying trends in the transformation of high-throughput data into networks, which are then used to design new catalysts that potentially result in high C₂ yields during the OCM reaction. Transforming high-throughput data in this manner has resulted in a representation of catalyst data that is more intuitive to use and also has resulted in the successful design of a myriad of catalysts that elicit high C₂ yields, several of which resulted in yields greater than those originally reported in the high-throughput data. Thus, transforming high-throughput catalytic data into catalyst design-friendly maps provides a new method of catalyst design that is more efficient and has a higher likelihood of resulting in high performance catalysts.

Catalyst data created through high-throughput experimentation is transformed into catalyst knowledge networks, leading to a new method of catalyst design where successfully designed catalysts result in high C₂ yields during the OCM reaction.