球面収差補正高分解能透過電子顕微鏡法と単色化内殻電子励起エネルギー損失分光法による酸化合成されたα-酸化鉄ナノウィスカー中の規則構造の研究

京都大学新制・課程博士博士(理学)甲第23459号理博第4753号新制||理||1681(附属図書館)京都大学大学院理学研究科化学専攻(主査)教授 倉田 博基, 教授 島川 祐一, 教授 寺西 利治学位規則第4条第1項該当Doctor of ScienceKyoto UniversityDGA

Understanding ordered structure in hematite nanowhiskers synthesized via thermal oxidation of iron-based substrates

Hematite (α-Fe₂O₃) nanowhiskers (NWs) with (001) basal faces synthesized via thermal oxidation of iron-based substrates are known to contain an ordered structure. The ordered structure has been identified to be related to oxygen vacancy ordering. However, the cause of its formation remains a mystery. In this study, with a high-resolution transmission electron microscopy (HR-TEM) investigation based on negative-Cs imaging (NCSI) and atomic-column position analysis, we observed tensile strain in the above-mentioned α-Fe₂O₃ NWs and revealed that the ordered structure was actually periodic interplanar gap expansions induced by oxygen vacancy accumulations. These findings were further confirmed in a monochromated electron energy loss spectroscopy (EELS) analysis of the α-Fe₂O₃ NWs. The EELS data indicated that, in comparison to pristine α-Fe₂O₃, the α-Fe₂O₃ NWs possessed expanded average Fesingle bondO and Osingle bondO interatomic distances and were oxygen-deficient. Clarifying oxygen deficiency in the α-Fe₂O₃ NWs was not attributed to an insufficient oxygen supply during the NW growth, we concluded the ordered structure formed to accommodate tensile strain in the α-Fe₂O₃ NWs. This work demonstrates the applicability of integrating NCSI and monochromated EELS for the examination of strain-induced microstructural and microchemical variations in lightly strained metal oxides

Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution

Convolutional neural networks have recently demonstrated high-quality reconstruction for single-image super-resolution. In this paper, we propose the Laplacian Pyramid Super-Resolution Network (LapSRN) to progressively reconstruct the sub-band residuals of high-resolution images. At each pyramid level, our model takes coarse-resolution feature maps as input, predicts the high-frequency residuals, and uses transposed convolutions for upsampling to the finer level. Our method does not require the bicubic interpolation as the pre-processing step and thus dramatically reduces the computational complexity. We train the proposed LapSRN with deep supervision using a robust Charbonnier loss function and achieve high-quality reconstruction. Furthermore, our network generates multi-scale predictions in one feed-forward pass through the progressive reconstruction, thereby facilitates resource-aware applications. Extensive quantitative and qualitative evaluations on benchmark datasets show that the proposed algorithm performs favorably against the state-of-the-art methods in terms of speed and accuracy.Comment: This work is accepted in CVPR 2017. The code and datasets are available on http://vllab.ucmerced.edu/wlai24/LapSRN

Asymmetrically interacting spreading dynamics on complex layered networks

The spread of disease through a physical-contact network and the spread of information about the disease on a communication network are two intimately related dynamical processes. We investigate the asymmetrical interplay between the two types of spreading dynamics, each occurring on its own layer, by focusing on the two fundamental quantities underlying any spreading process: epidemic threshold and the final infection ratio. We find that an epidemic outbreak on the contact layer can induce an outbreak on the communication layer, and information spreading can effectively raise the epidemic threshold. When structural correlation exists between the two layers, the information threshold remains unchanged but the epidemic threshold can be enhanced, making the contact layer more resilient to epidemic outbreak. We develop a physical theory to understand the intricate interplay between the two types of spreading dynamics.Comment: 29 pages, 14 figure