Off-axis MgB2 films using an in situ annealing pulsed laser deposition method

Highly smooth and c-axis oriented superconducting MgB2 thin films were prepared by pulsed laser deposition (PLD) with off-axis geometry. The films were deposited on Al2O3-C substrates perpendicularly aligned to a stoichiometric MgB2 target in a 120 mTorr high purity Ar background gas. An in situ annealing was carried out at 650C for 1 min in a 760 Torr Ar atmosphere. Despite the short annealing time, an x-ray theta-2 theta scan shows fairly good crystallization, according to the clear c-axis oriented peaks for the films. Both atomic force microscopy and the x-ray diffraction results indicate that the crystallite size is less than 50nm. The root mean square roughness of our off-axis film is ~4 nm in a 5x5 square micron area. The Tc onset value of the best off-axis film reaches 33.1 K with a narrow transition width of 0.9 K. The films showed no anisotropy in Hc2-T curves when parallel and perpendicular fields were applied. The slope of Hc2-T curves in low field regime is 1 T/K, which is among the highest reported values.Comment: 7 pages 7 figure

Multiple Fermi pockets revealed by Shubnikov-de Haas oscillations in WTe2

We use magneto-transport measurements to investigate the electronic structure of WTe2 single crystals. A non-saturating and parabolic magnetoresistance is observed in the temperature range between 2.5 to 200 K and magnetic fields up to 8 T. Shubnikov - de Haas oscillations with beating patterns are observed. The fast Fourier transform of the SdH oscillations reveals three oscillation frequencies, corresponding to three pairs of Fermi pockets with comparable effective masses , m* ~ 0.31 me. By fitting the Hall resistivity, we infer the presence of one pair of electron pockets and two pairs of hole pockets, together with nearly perfect compensation of the electron-hole carrier concentration. These magnetotransport measurements reveal the complex electronic structure in WTe2, explaining the nonsaturating magnetoresistance.Comment: Submitted to journal on 1 April, 2015, 4 Figure

Superconducting Properties of Carbonaceous Chemical Doped MgB2

The discovery of superconductivity in magnesium diboride (MgB2: 39 K, in January 2001) (Nagamatsu et al., 2001) has generated enormous interest and excitement in the superconductivity community and the world in general, but especially among researchers into superconductivity in non-oxide and boron related compounds

From Ontology to Semantic Similarity: Calculation of Ontology-Based Semantic Similarity

Advances in high-throughput experimental techniques in the past decade have enabled the explosive increase of omics data, while effective organization, interpretation, and exchange of these data require standard and controlled vocabularies in the domain of biological and biomedical studies. Ontologies, as abstract description systems for domain-specific knowledge composition, hence receive more and more attention in computational biology and bioinformatics. Particularly, many applications relying on domain ontologies require quantitative measures of relationships between terms in the ontologies, making it indispensable to develop computational methods for the derivation of ontology-based semantic similarity between terms. Nevertheless, with a variety of methods available, how to choose a suitable method for a specific application becomes a problem. With this understanding, we review a majority of existing methods that rely on ontologies to calculate semantic similarity between terms. We classify existing methods into five categories: methods based on semantic distance, methods based on information content, methods based on properties of terms, methods based on ontology hierarchy, and hybrid methods. We summarize characteristics of each category, with emphasis on basic notions, advantages and disadvantages of these methods. Further, we extend our review to software tools implementing these methods and applications using these methods

Artificial intelligence methods for oil and gas reservoir development: Current progresses and perspectives

Artificial neural networks have been widely applied in reservoir engineering. As a powerful tool, it changes the way to find solutions in reservoir simulation profoundly. Deep learning networks exhibit robust learning capabilities, enabling them not only to detect patterns in data, but also uncover underlying physical principles, incorporate prior knowledge of physics, and solve complex partial differential equations. This work presents the latest research advancements in the field of petroleum reservoir engineering, covering three key research directions based on artificial neural networks: data-driven methods, physics driven artificial neural network partial differential equation solver, and data and physics jointly driven methods. In addition, a wide range of neural network architectures are reviewed, including fully connected neural networks, convolutional neural networks, recurrent neural networks, and so on. The basic principles of these methods and their limitations in practical applications are also outlined. The future trends of artificial intelligence methods for oil and gas reservoir development are further discussed. The large language models are the most advanced neural networks so far, it is expected to be applied in reservoir simulation to predict the development performance.Document Type: PerspectiveCited as: Xue, L., Li, D., Dou, H. Artificial intelligence methods for oil and gas reservoir development: Current progresses and perspectives. Advances in Geo-Energy Research, 2023, 10(1): 65-70. https://doi.org/10.46690/ager.2023.10.0

Observation of topological transition of Fermi surface from a spindle-torus to a torus in large bulk Rashba spin-split BiTeCl

The recently observed large Rashba-type spin splitting in the BiTeX (X = I, Br, Cl) bulk states due to the absence of inversion asymmetry and large charge polarity enables observation of the transition in Fermi surface topology from spindle-torus to torus with varying the carrier density. These BiTeX systems with high spin-orbit energy scales offer an ideal platform for achieving practical spintronic applications and realizing non-trivial phenomena such as topological superconductivity and Majorana fermions. Here we use Shubnikov-de Haas oscillations to investigate the electronic structure of the bulk conduction band of BiTeCl single crystals with different carrier densities. We observe the topological transition of the Fermi surface (FS) from a spindle-torus to a torus. The Landau level fan diagram reveals the expected non-trivial {\pi} Berry phase for both the inner and outer FSs. Angle-dependent oscillation measurements reveal three-dimensional FS topology when the Fermi level lies in the vicinity of the Dirac point. All the observations are consistent with large Rashba spin-orbit splitting in the bulk conduction band.Comment: 28 pages, supplementary informatio