Toward the Creation of Highly Active Photocatalysts That Convert Methane into Methanol

Methane exists abundantly around Japan as methane hydrate. As the effective use of such methane, the conversion of methane into methanol has recently attracted much attention. Photocatalytic reaction is one of the methods which convert methane into methanol without using much energy. However, it is indispensable to improve the photocatalytic activity for their practical use. Our group has attempted to improve the activity of mesoporous tungsten trioxide and titanium dioxide (m-WO3 and m-TiO2) photocatalysts, which convert methane into methanol, by loading the ultrafine metal clusters as cocatalyst on the photocatalysts. As a result, we have succeeded in loading ultrafine metal-cluster cocatalysts onto m-WO3 and m-TiO2 and thereby improving their photocatalytic activity. Our study also demonstrated that the kind of metal element suitable for each photocatalyst depends on the kind of the photocatalysts, and thereby it is important to select the metal clusters suitable for each photocatalyst for improving its photocatalytic activity

ブラジル サンパウロ ジンブン カガク ケンキュウジョ シリョウ チョウサ チュウカン ホウコク

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ミンカン ダンタイ アーカイブズ ノ セイリ ヘンセイ キジュツ ノ ココロミ ニッポン リッコウカイ ナガタ シゲシ ブンショ オ ジレイ ニ

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ニホン ニオケル ミンカン シリョウ ノ ゲンジョウ ト コレカラ ノ カダイ

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クボ トオル セバタ ハジメ コッカ ト ヒミツ カクサレル コウブンショ

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Quantification of Local Morphodynamics and Local GTPase Activity by Edge Evolution Tracking

Advances in time-lapse fluorescence microscopy have enabled us to directly observe dynamic cellular phenomena. Although the techniques themselves have promoted the understanding of dynamic cellular functions, the vast number of images acquired has generated a need for automated processing tools to extract statistical information. A problem underlying the analysis of time-lapse cell images is the lack of rigorous methods to extract morphodynamic properties. Here, we propose an algorithm called edge evolution tracking (EET) to quantify the relationship between local morphological changes and local fluorescence intensities around a cell edge using time-lapse microscopy images. This algorithm enables us to trace the local edge extension and contraction by defining subdivided edges and their corresponding positions in successive frames. Thus, this algorithm enables the investigation of cross-correlations between local morphological changes and local intensity of fluorescent signals by considering the time shifts. By applying EET to fluorescence resonance energy transfer images of the Rho-family GTPases Rac1, Cdc42, and RhoA, we examined the cross-correlation between the local area difference and GTPase activity. The calculated correlations changed with time-shifts as expected, but surprisingly, the peak of the correlation coefficients appeared with a 6–8 min time shift of morphological changes and preceded the Rac1 or Cdc42 activities. Our method enables the quantification of the dynamics of local morphological change and local protein activity and statistical investigation of the relationship between them by considering time shifts in the relationship. Thus, this algorithm extends the value of time-lapse imaging data to better understand dynamics of cellular function