Testing the Debye Function Approach on a Laboratory X-ray Powder Diffraction Equipment. A Critical Study

Total Scattering Methods are nowadays widely used for the characterization of defective and nanosized materials. They commonly rely on highly accurate neutron and synchrotron diffraction data collected at dedicated beamlines. Here, we compare the results obtained on conventional laboratory equipment and synchrotron radiation when adopting the Debye Function Analysis method on a simple nanocrystalline material (a synthetic iron oxide with average particle size near to 10nm). Such comparison, which includes the cubic lattice parameter, the sample stoichiometry and the microstructural (size-distribution) analyses, highlights the limitations, but also some strengthening points, of dealing with conventional powder diffraction data collections on nanocrystalline material

Medium chain length (mcl)-pha-based nanocomposites for biomedical applications: system evaluation through xrd

Medium-chain polyhydroxyalkanoates (mcl-PHA) are flexible, elastomeric polymers produced by wide range of bacteria as intercellular storage of carbon and energy. They represent attractive components in biomaterial design because they are biocompatible, biodegradable and can be obtained using variety of carbon sources including waste streams[1]. However, being semi-crystalline, all mcl-PHAs are characterized by low melting temperature and poor tensile strength which can interfere with processing methods and wider biomedical application. Simple way to improve mcl-PHAs properties is to incorporate a nanophase within biopolymer to obtain nanocomposites. Nano-sized constituents interact with biopolymer more intimately affecting in turn the obtained nanocomposite properties as well as functionality. Among inorganic nanofillers, TiO2 nanostructures with high aspect ratio (e.g. nanofibers) have unique properties that support osteogenic phenotype which makes them suitable for bone tissue engineering [2]

Localized vs. delocalized character of charge carriers in LaAlO3/ SrTiO3 superlattices

Understanding the nature of electrical conductivity, superconductivity and magnetism between layers of oxides is of immense importance for the design of electronic devices employing oxide heterostructures. We demonstrate that resonant inelastic X-ray scattering can be applied to directly probe the carriers in oxide heterostructures. Our investigation on epitaxially grown LaAlO3/SrTiO3 superlattices unambiguously reveals the presence of both localized and delocalized Ti 3d carriers. These two types of carriers are caused by oxygen vacancies and electron transfer due to the polar discontinuity at the interface. This result allows explaining the reported discrepancy between theoretically calculated and experimentally measured carrier density values in LaAlO3/SrTiO3 heterostructures.Comment: 14 pages, 3 figure

Tetramethylbenzidine-TetrafluoroTCNQ: A narrow-gap semiconducting salt with room temperature relaxor ferroelectric behavior

We present an extension and revision of the spectroscopic and structural data of the mixed stack charge transfer (CT) crystal 3,3$^\prime$,5,5$^\prime$-tetramethylbenzidine--tetrafluoro-tetracyanoquinodimethane (TMB-TCNQF4), associated with new electric and dielectric measurements. Refinement of syncrotron structural data at low temperature has led to revise the previously reported [Phys. Rev. Mat. 2, 024602 (2018)] $C2/m$ structure. The revised structure is $P2_1/m$, with two dimerized stacks per unit cell, and is consistent with the vibrational data. However, polarized Raman data in the low-frequency region also indicate that by increasing temperature above 200 K the structure presents an increasing degree of disorder mainly along the stack axis. X-ray diffraction data at room temperature have confirmed that the correct structure is $P2_1/m$ -- no phase transitions -- but did not allow to definitely substantiate the presence of disorder. On the other hand, dielectric measurement have evidenced a typical relaxor ferroelectric behavior already at room temperature, with a peak in real part of dielectric constant $\epsilon'(T,\nu)$ around 200 K and 0.1 Hz. The relaxor behavior is explained in terms of the presence of spin solitons separating domains of opposite polarity that yield to ferroelectric nanodomains. TMB-TCNQF4 is confirmed to be a narrow gap band semiconductor ($E_a \sim 0.3$ eV) with room temperature conductivity of $\sim 10^{-4}~ \Omega^{-1}$ cm$^{-1}$.Comment: 21 pages, including the Supporting Information in the same file. Version 3 updates the x-ray structural data at room temperatur

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

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

Diffraction from Nanocrystal Superlattices

Diffraction from a lattice of periodically spaced crystals is a topic of current interest because of the great development of self-organised superlattices (SL) of nanocrystals (NC). The self-organisation of NC into SL has theoretical interest, but especially a rich application prospect, as the coherent organisation has large effects on a wide range of material properties. Diffraction is a key method to understand the type and quality of SL ordering. Hereby, the characteristic diffraction signature of an SL of NC—together with the characteristic types of disorder—are theoretically explored