22 research outputs found

    Evolution of the specific-heat anomaly of the high-temperature superconductor YBa2Cu3O7 under influence of doping through application of pressure up to 10 GPa

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    The evolution of the specific-heat anomaly in the overdoped range of a single crystal of the high-temperature superconductor YBa2Cu3O7 has been studied under influence of pressure up to 10 GPa, using AC calorimetry in a Bridgman-type pressure cell. We show that the specific-heat jump as well as the bulk Tc are reduced with increasing pressure in accordance with a simple charge-transfer model. This new method enables us through pressure-induced charge transfer to study the doping dependence of the superconducting transition, as well as the evolution of the superconducting condensation energy on a single stoichometric sample without adding atomic disorder.Comment: final version: J. Phys.: Condens. Matter 17 (2005) 4135-414

    Buried double CuO chains in YBa2_2Cu4_4O8_8 uncovered by nano-ARPES

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    The electron dynamics in the CuO chains has been elusive in Y-Ba-Cu-O cuprate systems by means of standard angle-resolved photoemission spectroscopy (ARPES); cleaved sample exhibits areas terminated by both CuO-chain or BaO layers, and the size of a typical beam results in ARPES signals that are superposed from both terminations. Here, we employ spatially-resolved ARPES with submicrometric beam (nano-ARPES) to reveal the surface-termination-dependent electronic structures of the double CuO chains in YBa2_2Cu4_4O8_8. We present the first observation of sharp metallic dispersions and Fermi surfaces of the double CuO chains buried underneath the CuO2_2-plane block on the BaO terminated surface. While the observed Fermi surfaces of the CuO chains are highly one-dimensional, the electrons in the CuO-chains do not undergo significant electron correlations and no signature of a Tomonaga-Luttinger liquid nor a marginal Fermi liquid is found. Our works represent an important experimental step toward understanding of the charge dynamics and provides a starting basis for modelling the high-TcT_c superconductivity in YBCO cuprate systems.Comment: 10 pages, 5 figures including supplementary material (4 pages, 2 figures

    High-pressure synthesis of superconducting Nb{1-x}B2 (x = 0-0.48) with the maximum Tc = 9.2 K

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    Superconductivity with Tc above 9 K was found in metal-deficient NbB2 prepared under 5 GPa, while no clear superconductivity was observed down to 3 K in stoichiometric NbB2. The superconductivity was observed above x = 0.04 in Nb1-xB2. and the lattice parameters also changed abruptly at x = 0.04. As x increased, the transition temperature Tc slightly rose and fell with the maximum value of 9.2 K at x = 0.24 for the samples sintered at 5 GPa and 1200 C. The Tc-value changed in the range from 7 K to 9 K, depending on the sintering pressure. A series of Ta1-xB2 (0 =< x =< 0.24) was also synthesized under high pressure to examine a special effect of high-pressure synthesis.Comment: 24 pages including 2 tables and 7 figures, accepted for publication in Physica

    Unsupervised clustering for identifying spatial inhomogeneity on local electronic structures

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    Spatial inhomogeneity on the electronic structure is one of the vital keys to provide a better understanding of the emergent quantum phenomenon. Given the recent developments on spatially-resolved ARPES (ARPES: angle-resolved photoemission spectroscopy), the information on the spatial inhomogeneity on the local electronic structure is now accessible. However, the next challenge becomes apparent as the conventional analysis encounters difficulty handling a large volume of a spatial mapping dataset, typically generated in the spatially-resolved ARPES experiments. Here, we propose a machine-learning-based approach using unsupervised clustering algorithms (K-means and fuzzy-c-means) to examine the spatial mapping dataset. Our analysis methods enable automated categorization of the spatial mapping dataset with a much-reduced human intervention and workload, thereby allowing quick identification and visualization of the spatial inhomogeneity on the local electronic structures

    Unsupervised Learning for Identifying Surface Inhomogeneity on Electronic Structures of High-Tc Cuprate

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    Angle-resolved photoemission spectroscopy (ARPES) is a powerful experimental technique in modern materials science because it can directly probe electronic states, which are deeply related to the physical properties of materials. Among the advanced ARPES techniques, spatially-resolved ARPES has recently attracted growing interest because of its capability to obtain local electronic information at the micro- or nano-metric length scales by utilizing a well-focused light source [1]. On the other hand, it is not trivial to analyze and understand the spatial variation of electronic states against massive datasets, typically in 4-dimensional space (energy, momentum, and two spatial axes). In this work, we will present unsupervised learning using K-means and fuzzy-c-means clustering methods on spatial mapping dataset taken from Y-based high-Tc cuprate superconductor (YBa2Cu3O7-) by micro-ARPES. The spatial mapping dataset clearly showed spatial inhomogeneity on electronic structures due to multiple surface terminations due to BaO or CuO layers on a cleavage (001) plane [2]. We will present how the clustering analysis enables the visualization and identification of such spatial inhomogeneity on the local electronic structures. The advantages and disadvantages of these clustering methods will be detailed, with a comparison of the conventional analysis method.[1] Hideaki Iwasawa, Electronic Structure 2, 043001 (2020).[2] H. Iwasawa et al., Phys. Rev. B 98, 081112(R) (2018).The 9th International Symposium on Surface Science (ISSS-9

    異議を留めない承諾前の第三取得者と抵当権の復活

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    In regard to the assignment of obligations, if the debtor has consented without objection, then according to Clause 468 Paragraph 1 of the Civil Code, even if the debtor is opposed to the assignee, they cannot oppose the assignee in this context. However, if it is transfered after the secured claims of this mortgage have been extinguished by settlement, then in regard to this transfer, if the debtor consents without objection, then the mortgage should not be revived to a third party acquirer that has existed since before the settlement

    Unsupervised Clustering of Spatially-resolved ARPES Data

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    Angle-resolved photoemission spectroscopy (ARPES) is a powerful experimental technique in modern materials science because it can directly probe electronic states, which are directly related to the physical properties of materials. Among the advanced ARPES techniques, spatially-resolved ARPES has recently attracted growing interest because of its capability to obtain local electronic information at the micro- or nano-metric length scales by utilizing a well-focused light source [1]. On the other hand, it is not trivial to analyze and understand the spatial variation of electronic states against massive datasets, typically in 4-dimensional space (energy, momentum, and two spatial axes). In this work, we present unsupervised clustering analyses based on K-means and Fuzzy-c-means methods on spatially-resolved micro-ARPES data from Y-based high-Tc cuprate superconductor YBa2Cu3O7-δ, which shows spatial inhomogeneity due to multiple surface terminations due to BaO or CuO layers on a cleavage (001) plane [2]. The performance of the clustering analyses will be demonstrated with the comparison of the conventional analysis method.[1] Hideaki Iwasawa, Electronic Structure 2, 043001 (2020).[2] H. Iwasawa et al., Phys. Rev. B 98, 081112(R) (2018).The 11th New Generation in Strongly Correlated Electron System
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