STM/STS study on electronic superstructures in the superconducting state of high-T-c cuprate Bi2Sr2CaCu2O8+delta

We report STM/STS measurements at 8 K in underdoped Bi2Sr2CaCu2O8+delta crystals (T-c = 76 K and hole-doping level p similar to 0.12) whose energy spectra around the Fermi level are characterized by a two-gap structure consisting of spatially inhomogeneous pseudogap (PG) and comparatively homogeneous superconducting gap (SCG). Two electronic superstructures, checkerboard modulation (CBM) and Cu-O-Cu bond-centered modulation (BCM), are observed with mapping spectral weights at low energies within the SCG and the ratio of spectral weights at +/-Delta(PG) (PG energy), respectively. On the basis of the present findings, we suggest that the lower-energy scale CBM is an intrinsic property of Cu-O planes and can coexist with the BCM whose characteristic energy is similar to Delta(PG) in identical regions in real space

Anomalous Transport Properties in BiS2-based Superconductors LnO1−xFxBiS2 (Ln = Nd, La-Sm)

We report the electronic properties of the layered bismuth-based sulfide superconductors NdO1−xFxBiS2 (x = 0.25, 0.4, and 0.5) and La1−ySmyO0.5F0.5BiS2 (y = 0.1–0.7), which have been studied by investigation of their transport properties and X-ray diffraction. In the lightly carrier-doped NdO1−xFxBiS2 (x = 0.25 and 0.4) and La1−ySmyO0.5F0.5BiS2 (y = 0.3 and 0.4), the resistivity and Hall coefficient exhibit anomalous temperature dependences below TCDW ∼ 130 and 200 K, respectively, suggesting the formation of an energy gap on the Fermi surface associated with charge-density wave (CDW). In NdO1−xFxBiS2 (x = 0.25), the bond angles and bond lengths of the Bi–S pentahedron change their temperature dependences below ∼200 K, suggesting that a lattice instability related to the Bi–S pentahedron exists below ∼200 K, which is much higher than TCDW. These results indicate that the lattice instability of the Bi–S pentahedron can trigger a CDW transition in the low-carrier region of BiS2 superconductors

Field-induced hexagonal to square transition of the vortex lattice in overdoped La{1.8}Sr{0.2}CuO{4}

We report on a small angle neutron scattering study of the vortex lattice in overdoped La{2-x}Sr{x}CuO{4} (x=0.2) up to high magnetic fields (9.5 Tesla) applied perpendicular to the CuO2 planes. At low magnetic fields we observe a crossover from hexagonal to square coordination of the vortex lattice. This field-induced transition confirms the results obtained in slightly overdoped La{2-x}Sr{x}CuO{4} (x=0.17).Comment: 3 pages, 2 figures. to appear in Physica C (proceedings for the M2S-HTSC-VII Conference, May 25-30, Rio de Janeiro

Crystal Symmetry of Stripe Ordered La1.88Sr0.12CuO4

We present a combined x-ray and neutron diffraction study of the stripe ordered superconductor \lscox{0.12}. The average crystal structure is consistent with the orthorhombic $Bmab$ space group as commonly reported in the literature. This structure however is not symmetry compatible with a second order phase transition into the stripe order phase, and, as we report here numerous Bragg peaks forbidden in the $Bmab$ space group are observed. We have studied and analysed these $Bmab$-forbidden Bragg reflections. Fitting of the diffraction intensities yields monoclinic lattice distortions that are symmetry consistent with charge stripe order.Comment: 7 pages, 3 figures, 5 Table

Impurity Effects on the Energy Gap in Fe-doped Bi2212

We performed scanning tunnelling microscopy/spectroscopy (STM/STS) on Fe-doped Bi2212. The Fe substitution for Cu causes a strong spatial inhomogeneity in STS spectra. The energy gap (Δ1 ∼80mV) has a sub-gap (Δ2 ∼70mV) in some distinct locations on the sample surface. We find that the gap edge peaks are largely depressed and only the sub-gap survives across the region where the spatial modulation of the local density of states is stronger. This indicates, that Δ1 anti-correlates with Δ2.Conference : 20th International Conference on Magnetism, ICM 2015Location : Barcelona, SPAINDate : JUL 05-10, 201

Single-domain stripe order in a high-temperature superconductor

The coupling of spin, charge and lattice degrees of freedom results in the emergence of novel states of matter across many classes of strongly correlated electron materials. A model example is unconventional superconductivity, which is widely believed to arise from the coupling of electrons via spin excitations. In cuprate high-temperature superconductors, the interplay of charge and spin degrees of freedom is also reflected in a zoo of charge and spin-density wave orders that are intertwined with superconductivity. A key question is whether the different types of density waves merely coexist or are indeed directly coupled. Here we profit from a neutron scattering technique with superior beam-focusing that allows us to probe the subtle spin-density wave order in the prototypical high-temperature superconductor La$${}_{1.88}$$Sr$${}_{0.12}$$CuO$${}_{4}$$ under applied uniaxial pressure to demonstrate that the two density waves respond to the external tuning parameter in the same manner. Our result shows that suitable models for high-temperature superconductivity must equally account for charge and spin degrees of freedom via uniaxial charge-spin stripe fluctuations

Weak-signal extraction enabled by deep-neural-network denoising of diffraction data

Removal or cancellation of noise has wide-spread applications for imaging and acoustics. In every-day-life applications, denoising may even include generative aspects which are unfaithful to the ground truth. For scientific applications, however, denoising must reproduce the ground truth accurately. Here, we show how data can be denoised via a deep convolutional neural network such that weak signals appear with quantitative accuracy. In particular, we study X-ray diffraction on crystalline materials. We demonstrate that weak signals stemming from charge ordering, insignificant in the noisy data, become visible and accurate in the denoised data. This success is enabled by supervised training of a deep neural network with pairs of measured low- and high-noise data. This way, the neural network learns about the statistical properties of the noise. We demonstrate that using artificial noise (such as Poisson and Gaussian) does not yield such quantitatively accurate results. Our approach thus illustrates a practical strategy for noise filtering that can be applied to challenging acquisition problems.Comment: 8 pages, 4 figure