Vacuum cleaving of superconducting niobium tips to optimize noise filtering and with adjustable gap size for scanning tunneling microscopy

Superconducting (SC) tips for scanning tunneling microscopy (STM) can enhance a wide range of surface science studies because they offer exquisite energy resolution, allow the study of Josephson tunneling, or provide spatial contrast based on the local interaction of the SC tip with the sample. The appeal of a SC tip is also practical. An SC gap can be used to characterize and optimize the noise of a low-temperature apparatus. Unlike typical samples, SC tips can be made with less ordered materials, such as from SC polycrystalline wires or by coating a normal metal tip with a superconductor. Those recipes either require additional laboratory infrastructure or are carried out in ambient conditions, leaving an oxidized tip behind. Here, we revisit the vacuum cleaving of an Nb wire to prepare fully gapped tips in an accessible one-step procedure. To show their utility, we measure the SC gap of Nb on Au(111) to determine the base temperature of our microscope and to optimize its RF filtering. The deliberate coating of the Nb tip with Au fully suppresses the SC gap and we show how sputtering with Ar+ ions can be used to gradually recover the gap, promising tunability for tailored SC gaps sizes. • Oxide free superconducting STM tips • RF filter optimization

Fast spectroscopic mapping of two-dimensional quantum materials

The discovery of quantum materials entails extensive spectroscopic studies that are carried out against multitudes of degrees of freedom, such as magnetic field, location, temperature, or doping. As this traditionally involves two or more serial measurement tasks, spectroscopic mapping can become excruciatingly slow. We demonstrate orders of magnitude faster measurements through our combination of sparse sampling and parallel spectroscopy. We exemplify our concept using quasiparticle interference imaging of Au(111) and Bi2Sr2CaCu2O8+δ (Bi2212), as two well-known model systems. Our method is accessible, straightforward to implement with existing setups, and can be easily extended to promote gate or field spectroscopy. In view of further substantial speed advantages, it is setting the stage to fundamentally promote the discovery of quantum materials

Procyanidins in Theobroma cacao Reduce Plasma Cholesterol Levels in High Cholesterol-Fed Rats

We evaluated the effect of cacao procyanidins (CP) on plasma lipid levels in high cholesterol-fed rats. Animals were divided into 4 groups, and each group was fed on either a normal diet, high cholesterol diet (HCD) containing 1% cholesterol (HCD without CP), HCD with 0.5% (HCD with 0.5% CP) or 1.0% CP (HCD with 1.0% CP) for 4 weeks. Plasma cholesterol level was significantly higher in the HCD without CP group than the normal diet group (p<0.01). Supplementation of CP significantly decreased plasma cholesterol (p<0.01) to levels similar to those of the normal diet group. The liver cholesterol and triglyceride levels in all HCD groups were significantly higher (p<0.01), but 1.0% CP feeding significantly reduced this increase. Fecal excretion of neutral sterol and triglyceride was significantly increased in all HCD groups (p<0.01), and the excreted amounts tended to be higher in the HCD with CP groups. The procyanidins dose-dependently reduced micellar solubility of cholesterol and this activity increased with increasing molecular weight. These results suggest that one of the mechanisms of CP to lower plasma cholesterol is inhibition of intestinal absorption of cholesterol

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

Osteoconductivity of hydrothermally synthesized beta-tricalcium phosphate composed of rod-shaped particles under mechanical unloading

Spherical beta-tricalcium phosphate (β-TCP) granules synthesized using a unique dropping slurry method expressed good osteoconductivity with prominent bone apposition and bioresorbability when implanted into the rat femur (Gonda et al, Key Eng. Mater. 361-363:1013-1016, 2008). The spherical β-TCP granules were implanted into the bone defect created in the distal end of the right femur of each 8-week-old female Wistar rat. To analyze performance of the spherical β-TCP granules as bone substitute in the bone with reduction in osteogenic potential, the right sciatic neurectomy was performed after implantation and the right hind limb was kept unloaded for 2 weeks before euthanization. Four weeks after implantation, some spherical β-TCP granules with resorption in part were surrounded by newly formed bone. Eight and 12 weeks after implantation, most of the residual β-TCP granules were embedded in newly formed bone, and total volume of the implant and newly formed bone was more than the other portions of the bone or the bone of control animals. Osteoclast activity in the implanted area was also higher than the other portions of the bone or the bone of control animals. Replacement of the intraosseous residual β-TCP granules for bone progressed at 12 weeks after implantation compared to those at 8 weeks after implantation. These data suggested that the spherical β-TCP granules stimulated osteogenesis and osteoclast activity of the unloaded bone

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

Hidden magnetism at the pseudogap critical point of a high temperature superconductor

The mysterious pseudogap phase of cuprate superconductors ends at a critical hole doping level p* but the nature of the ground state below p* is still debated. Here, we show that the genuine nature of the magnetic ground state in La2-xSrxCuO4 is hidden by competing effects from superconductivity: applying intense magnetic fields to quench superconductivity, we uncover the presence of glassy antiferromagnetic order up to the pseudogap boundary p* ~ 0.19, and not above. There is thus a quantum phase transition at p*, which is likely to underlie highfield observations of a fundamental change in electronic properties across p*. Furthermore, the continuous presence of quasi-static moments from the insulator up to p* suggests that the physics of the doped Mott insulator is relevant through the entire pseudogap regime and might be more fundamentally driving the transition at p* than just spin or charge ordering.Comment: 26 pages, supplementary info include

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

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

The removal or cancellation of noise has wide-spread applications in imaging and acoustics. In applications in everyday life, such as image restoration, denoising may even include generative aspects, which are unfaithful to the ground truth. For scientific use, however, denoising must reproduce the ground truth accurately. Denoising scientific data is further challenged by unknown noise profiles. In fact, such data will often include noise from multiple distinct sources, which substantially reduces the applicability of simulation-based approaches. Here we show how scientific data can be denoised by using a deep convolutional neural network such that weak signals appear with quantitative accuracy. In particular, we study X-ray diffraction and resonant X-ray scattering data recorded 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. We additionally show that using artificial noise 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