Diffraction Imaging Research Articles

Quantitative diffraction imaging using attosecond pulses

We have proposed and developed a method to utilize attosecond pulses in diffraction imaging techniques applied to complex samples. In this study, the effects of the broadband properties of the wavefield owing to attosecond pulses are considered in the reconstruction of images through the decomposition of the broad spectrum into multi-spectral components. This method successfully reconstructs the multi-spectral information of complex samples, probes, and spectral bandwidths using broadband diffraction intensities generated from computational scanning experiments. The results obtained in this research open the opportunities to perform quantitative ultrafast imaging using the attosecond pulses.

Improving the high-voltage behavior of LiNiO2 through electrochemical property regulation by Mg doping

In this study, high-temperature solid-phase techniques are used to prepare pristine and Mg-modified LiNiO2 cathode materials. Various characterizations are performed to analyze the morphology, structure, and electrical state of the two materials. Electrochemical tests show that Mg doping effectively improves the electrochemical properties of the materials. In particular, the modified sample exhibits a higher specific discharge capacity of 244.5 mA h g−1 than the pristine sample. Moreover, its maximum capacity retention after 304 cycles at 1 C is 54.1%. In situ X-ray powder diffraction imaging reveals that the modified material has better phase-transition reversibility than the pristine material. Density functional theory calculations indicate that Mg-doped Li sites can accelerate Li+ transport. Atomic force microscopy results demonstrate that Mg-doped Li sites act as supportive skeletons that enhance the mechanical properties of the material. Therefore, the proposed strategy for the Mg doping of Li sites can improve the architecture of LiNiO2 cathode materials.

Helium-electrospray improves sample delivery in X-ray single-particle imaging experiments

Imaging the structure and observing the dynamics of isolated proteins using single-particle X-ray diffractive imaging (SPI) is one of the potential applications of X-ray free-electron lasers (XFELs). Currently, SPI experiments on isolated proteins are limited by three factors: low signal strength, limited data and high background from gas scattering. The last two factors are largely due to the shortcomings of the aerosol sample delivery methods in use. Here we present our modified electrospray ionization (ESI) source, which we dubbed helium-ESI (He-ESI). With it, we increased particle delivery into the interaction region by a factor of 10, for 26 nm-sized biological particles, and decreased the gas load in the interaction chamber corresponding to an 80% reduction in gas scattering when compared to the original ESI. These improvements have the potential to significantly increase the quality and quantity of SPI diffraction patterns in future experiments using He-ESI, resulting in higher-resolution structures.

Revealing the Distribution of Lithium Compounds in Lithium Dendrites by Four-Dimensional Electron Microscopy Analysis.

Characterizing the microstructure of radiation- and chemical-sensitive lithium dendrites and its solid electrolyte interphase (SEI) is an important task when investigating the performance and reliability of lithium-ion batteries. Widely used methods, such as cryogenic high-resolution transmission electron microscopy as well as related spectroscopy, are able to reveal the local structure at nanometer and atomic scale; however, these methods are unable to show the distribution of various crystal phases along the dendrite in a large field of view. In this work, two types of four-dimensional electron microscopy diffractive imaging methods, i.e., scanning electron nanodiffraction (SEND) and scanning convergent beam electron diffraction (SCBED), are employed to show a new pathway on characterizing the sensitive lithium dendrite samples at room temperature and in a large field of view. Combining with the non-negative matrix factorization (NMF) algorithm, orientations of different lithium metal grains along the lithium dendrite as well as different lithium compounds in the SEI layer are clearly identified.

Designable Synthesis of Layered Silicates and Tunable Interlayer Expanded to Zeolites.

Layered zeolitic silicates and corresponding interlayer-expanded porous materials exhibit attractive application potential in wide fields. Nonetheless, designable synthesis and structure analysis of layered silicates remain challenging. Herein, two kinds of layered silicates are synthesized using different di-quaternary ammonium-type organic structure-directing agents (OSDAs). Their crystal structures are analyzed and verified by 3D electron diffraction (3D ED) and high-resolution TEM imaging. The suitable configurations of OSDA can lead to desirable interlayer states. Additionally, two new zeolite structures both with 12-membered ring (MR) channels intersected by 8 MR channels and larger interlayer spaces are constructed from layered silicate precursors by interlayer silylation. The new zeolitic material exhibits potential application in adsorption of organic pollution and catalytic reaction. This study is expected to develop versatile ways for the design and synthesis of layered silicates even zeolites and provide references in characterizing layered materials and zeolites as well.

ProSPyX: software for post-processing images of X-ray ptychography with spectral capabilities.

X-ray ptychography is a coherent diffraction imaging technique based on acquiring multiple diffraction patterns obtained through the illumination of the sample at different partially overlapping probe positions. The diffraction patterns collected are used to retrieve the complex transmittivity function of the sample and the probe using a phase retrieval algorithm. Absorption or phase contrast images of the sample as well as the real and imaginary parts of the probe function can be obtained. Furthermore, X-ray ptychography can also provide spectral information of the sample from absorption or phase shift images by capturing multiple ptychographic projections at varying energies around the resonant energy of the element of interest. However, post-processing of the images is required to extract the spectra. To facilitate this, ProSPyX, a Python package that offers the analysis tools and a graphical user interface required to process spectral ptychography datasets, is presented. Using the PyQt5 Python open-source module for development and design, the software facilitates extraction of absorption and phase spectral information from spectral ptychographic datasets. It also saves the spectra in file formats compatible with other X-ray absorption spectroscopy data analysis software tools, streamlining integration into existing spectroscopic data analysis pipelines. To illustrate its capabilities, ProSPyX was applied to process the spectral ptychography dataset recently acquired on a nickel wire at the SWING beamline of the SOLEIL synchrotron.

Phaseless Sampling on Square-Root Lattices

AbstractDue to its appearance in a remarkably wide field of applications, such as audio processing and coherent diffraction imaging, the short-time Fourier transform (STFT) phase retrieval problem has seen a great deal of attention in recent years. A central problem in STFT phase retrieval concerns the question for which window functions$$g \in {L^2({\mathbb R}^d)}$$g∈L2(Rd)and which sampling sets$$\Lambda \subseteq {\mathbb R}^{2d}$$Λ⊆R2dis every$$f \in {L^2({\mathbb R}^d)}$$f∈L2(Rd)uniquely determined (up to a global phase factor) by phaseless samples of the form$$\begin{aligned} |V_gf(\Lambda )| = \left\{ |V_gf(\lambda )|: \lambda \in \Lambda \right\} , \end{aligned}$$|Vgf(Λ)|=|Vgf(λ)|:λ∈Λ,where$$V_gf$$Vgfdenotes the STFT offwith respect tog. The investigation of this question constitutes a key step towards making the problem computationally tractable. However, it deviates from ordinary sampling tasks in a fundamental and subtle manner: recent results demonstrate that uniqueness is unachievable if$$\Lambda $$Λis a lattice, i.e$$\Lambda = A{\mathbb Z}^{2d}, A \in \textrm{GL}(2d,{\mathbb R})$$Λ=AZ2d,A∈GL(2d,R). Driven by this discretization barrier, the present article centers around the initiation of a novel sampling scheme which allows for unique recovery of any square-integrable function via phaseless STFT-sampling. Specifically, we show that square-root lattices, i.e., sets of the form$$\begin{aligned} \Lambda = A \left( \sqrt{{\mathbb Z}} \right) ^{2d}, \ \sqrt{{\mathbb Z}} = \{ \pm \sqrt{n}: n \in {\mathbb N}_0 \}, \end{aligned}$$Λ=AZ2d,Z={±n:n∈N0},guarantee uniqueness of the STFT phase retrieval problem. The result holds for a large class of window functions, including Gaussians

Unsupervised machine learning combined with 4D scanning transmission electron microscopy for bimodal nanostructural analysis

Unsupervised machine learning techniques have been combined with scanning transmission electron microscopy (STEM) to enable comprehensive crystal structure analysis with nanometer spatial resolution. In this study, we investigated large-scale data obtained by four-dimensional (4D) STEM using dimensionality reduction techniques such as non-negative matrix factorization (NMF) and hierarchical clustering with various optimization methods. We developed software scripts incorporating knowledge of electron diffraction and STEM imaging for data preprocessing, NMF, and hierarchical clustering. Hierarchical clustering was performed using cross-correlation instead of conventional Euclidean distances, resulting in rotation-corrected diffractions and shift-corrected maps of major components. An experimental analysis was conducted on a high-pressure-annealed metallic glass, Zr-Cu-Al, revealing an amorphous matrix and crystalline precipitates with an average diameter of approximately 7 nm, which were challenging to detect using conventional STEM techniques. Combining 4D-STEM and optimized unsupervised machine learning enables comprehensive bimodal (i.e., spatial and reciprocal) analyses of material nanostructures.

FDIP—A Fast Diffraction Image Processing Library for X-ray Crystallography Experiments

Serial crystallography (SX) is a cutting-edge technique in structural biology, involving the systematic collection of X-ray diffraction data from numerous randomly oriented microcrystals. To extract comprehensive three-dimensional information about the studied system, SX utilises thousands of measured diffraction patterns. As such, SX takes advantages of the properties of modern X-ray sources, including Free Electron Lasers (FELs) and third and fourth generation synchrotrons, as well as contemporary high-repetition-rate detectors. Efficient analysis of the extensive datasets generated during SX experiments demands fast and effective algorithms. The FDIP library offers meticulously optimised functions tailored for preprocessing data obtained in SX experiments. This encompasses tasks such as background subtraction, identification and masking of parasitic streaks, elimination of unwanted powder diffraction (e.g., from ice or salt crystals), and pinpointing useful Bragg peaks in each diffraction pattern. The library is equipped with a user-friendly graphical interface for facile parameter adjustment tailored to specific datasets. Compatible with popular SX processing software like OnDA, Cheetah, CrystFEL, and Merge3D, the FDIP library enhances the capabilities of these tools for streamlined and precise serial crystallography analyses.

Effect of Graphene on the Performance of Silicon-Carbon Composite Anode Materials for Lithium-Ion Batteries.

(Si/graphite)@C and (Si/graphite/graphene)@C were synthesized by coating asphalt-cracked carbon on the surface of a Si-based precursor by spray drying, followed by heat treatment at 1000 °C under vacuum for 2h. The impact of graphene on the performance of silicon-carbon composite-based anode materials for lithium-ion batteries (LIBs) was investigated. Transmission electron microscopy (TEM) and selected area electron diffraction (SAED) images of (Si/graphite/graphene)@C showed that the nano-Si and graphene particles were dispersed on the surface of graphite, and thermogravimetric analysis (TGA) curves indicated that the content of silicon in the (Si/graphite/graphene)@C was 18.91%. More bituminous cracking carbon formed on the surface of the (Si/graphite/graphene)@C due to the large specific surface area of graphene. (Si/Graphite/Graphene)@C delivered first discharge and charge capacities of 860.4 and 782.1 mAh/g, respectively, initial coulombic efficiency (ICE) of 90.9%, and capacity retention of 74.5% after 200 cycles. The addition of graphene effectively improved the cycling performance of the Si-based anode materials, which can be attributed to the reduction of electrochemical polarization due to the good structural stability and high conductivity of graphene.

All-optical image denoising using a diffractive visual processor

Image denoising, one of the essential inverse problems, targets to remove noise/artifacts from input images. In general, digital image denoising algorithms, executed on computers, present latency due to several iterations implemented in, e.g., graphics processing units (GPUs). While deep learning-enabled methods can operate non-iteratively, they also introduce latency and impose a significant computational burden, leading to increased power consumption. Here, we introduce an analog diffractive image denoiser to all-optically and non-iteratively clean various forms of noise and artifacts from input images – implemented at the speed of light propagation within a thin diffractive visual processor that axially spans <250 × λ, where λ is the wavelength of light. This all-optical image denoiser comprises passive transmissive layers optimized using deep learning to physically scatter the optical modes that represent various noise features, causing them to miss the output image Field-of-View (FoV) while retaining the object features of interest. Our results show that these diffractive denoisers can efficiently remove salt and pepper noise and image rendering-related spatial artifacts from input phase or intensity images while achieving an output power efficiency of ~30–40%. We experimentally demonstrated the effectiveness of this analog denoiser architecture using a 3D-printed diffractive visual processor operating at the terahertz spectrum. Owing to their speed, power-efficiency, and minimal computational overhead, all-optical diffractive denoisers can be transformative for various image display and projection systems, including, e.g., holographic displays.

Enhancing electrospray ionization efficiency forparticle transmission through an aerodynamic lens stack.

This work investigates the performance of the electrospray aerosol generator at the European X-ray Free Electron Laser (EuXFEL). This generator is, together with an aerodynamic lens stack that transports the particles into the X-ray interaction vacuum chamber, the method of choice to deliver particles for single-particle coherent diffractive imaging (SPI) experiments at the EuXFEL. For these experiments to be successful, it is necessary to achieve high transmission of particles from solution into the vacuum interaction region. Particle transmission is highly dependent on efficient neutralization of the charged aerosol generated by the electrospray mechanism as well as the geometry in the vicinity of the Taylor cone. We report absolute particle transmission values for different neutralizers and geometries while keeping the conditions suitable for SPI experiments. Our findings reveal that a vacuum ultraviolet ionizer demonstrates a transmission efficiency approximately seven times greater than the soft X-ray ionizer used previously. Combined with an optimized orifice size on the counter electrode, we achieve >40% particle transmission from solution into the X-ray interaction region. These findings offer valuable insights for optimizing electrospray aerosol generator configurations and data rates for SPI experiments.

On the scalability of helium-filled soap bubbles for volumetric PIV

The scalability of experiments using PIV relies upon several parameters, namely illumination power, camera sensor and primarily the tracers light scattering capability. Given their larger cross section, helium-filled soap bubbles (HFSB) allow measurements in air flows over a significantly large domain compared to traditional oil or fog droplets. Controlling their diameter translates into scalability of the experiment. This work presents a technique to extend the control of HFSB diameter by geometrical variations of the generator. The latter expands the more limited range allowed by varying the relative helium-air mass flow rates. A theoretical model predicts the bubble size and production rate, which is verified experimentally by high-speed shadow visualization. The overall range of HFSB produced in a stable (bubbling) regime varies from 0.16 to 2.7 mm. Imaging by light scattering of such tracers is also investigated, in view of controversies in the literature on whether diffraction or geometrical imaging dominate the imaging regime. The light scattered by scaled HFSB tracers is imaged with a high-speed camera orthogonal to the illumination. Both the total energy collected on the sensor for a single tracer, as well as its peak intensity, are found to preserve scaling with the square of the diameter at object magnification of 10–1 or below, typical of PIV experiments. For large-scale volumetric applications, it is shown that varying the bubble diameter allows increasing both the measurement domain as well as the working distance of the imagers at 10 m and beyond. A scaling rule is proposed for the latter.Graphical abstract

An Inversion Scheme for Elastic Diffraction Tomography Based on Mode Separation

An Inversion Scheme for Elastic Diffraction Tomography Based on Mode Separation

Towards the Theory of X-ray Diffraction Tomography of Crystals with Nanosized Defects

Towards the Theory of X-ray Diffraction Tomography of Crystals with Nanosized Defects

Q-RBSA: high-resolution 3D EBSD map generation using an efficient quaternion transformer network

Gathering 3D material microstructural information is time-consuming, expensive, and energy-intensive. Acquisition of 3D data has been accelerated by developments in serial sectioning instrument capabilities; however, for crystallographic information, the electron backscatter diffraction (EBSD) imaging modality remains rate limiting. We propose a physics-based efficient deep learning framework to reduce the time and cost of collecting 3D EBSD maps. Our framework uses a quaternion residual block self-attention network (QRBSA) to generate high-resolution 3D EBSD maps from sparsely sectioned EBSD maps. In QRBSA, quaternion-valued convolution effectively learns local relations in orientation space, while self-attention in the quaternion domain captures long-range correlations. We apply our framework to 3D data collected from commercially relevant titanium alloys, showing both qualitatively and quantitatively that our method can predict missing samples (EBSD information between sparsely sectioned mapping points) as compared to high-resolution ground truth 3D EBSD maps.

Enhanced Piezoelectric Response at Nanoscale Vortex Structures in Ferroelectrics.

The piezoelectric response is a measure of the sensitivity of a material's polarization to stress or its strain to an applied field. Using in operando X-ray Bragg coherent diffraction imaging, we observe that topological vortices are the source of a 5-fold enhancement of the piezoelectric response near the vortex core. The vortices form where several low-symmetry ferroelectric phases and phase boundaries coalesce. Unlike bulk ferroelectric solid solutions in which a large piezoelectric response is associated with coexisting phases in the proximity of the triple point, the largest responses for pure BaTiO3 at the nanoscale are in spatial regions of extremely small spontaneous polarization at vortex cores. The response decays inversely with polarization away from the vortex, analogous to the behavior in bulk ceramics as the cation compositions are varied away from the triple point. We use first-principles-based molecular dynamics to augment our observations, and our results suggest that nanoscale piezoelectric materials with a large piezoelectric response can be designed within a parameter space governed by vortex cores. Our findings have implications for the development of next-generation nanoscale piezoelectric materials.

Ultrafast Bragg coherent diffraction imaging of epitaxial thin films using deep complex-valued neural networks

Domain wall structures form spontaneously due to epitaxial misfit during thin film growth. Imaging the dynamics of domains and domain walls at ultrafast timescales can provide fundamental clues to features that impact electrical transport in electronic devices. Recently, deep learning based methods showed promising phase retrieval (PR) performance, allowing intensity-only measurements to be transformed into snapshot real space images. While the Fourier imaging model involves complex-valued quantities, most existing deep learning based methods solve the PR problem with real-valued based models, where the connection between amplitude and phase is ignored. To this end, we involve complex numbers operation in the neural network to preserve the amplitude and phase connection. Therefore, we employ the complex-valued neural network for solving the PR problem and evaluate it on Bragg coherent diffraction data streams collected from an epitaxial La2-xSrxCuO4 (LSCO) thin film using an X-ray Free Electron Laser (XFEL). Our proposed complex-valued neural network based approach outperforms the traditional real-valued neural network methods in both supervised and unsupervised learning manner. Phase domains are also observed from the LSCO thin film at an ultrafast timescale using the complex-valued neural network.

Convolutional neural network model for automatic recognition and classification of pancreatic cancer cell based on analysis of lipid droplet on unlabeled sample by 3D optical diffraction tomography

IntroductionPancreatic cancer cells generally accumulate large numbers of lipid droplets (LDs), which regulate lipid storage. To promote rapid diagnosis, an automatic pancreatic cancer cell recognition system based on a deep convolutional neural network was proposed in this study using quantitative images of LDs from stain-free cytologic samples by optical diffraction tomography. MethodsWe retrieved 3D refractive index tomograms and reconstructed 37 optical images of one cell. From the four cell lines, the obtained fields were separated into training and test datasets with 10,397 and 3,478 images, respectively. Furthermore, we adopted several machine learning techniques based on a single image-based prediction model to improve the performance of the computer-aided diagnostic system. ResultsPancreatic cancer cells had a significantly lower total cell volume and dry mass than did normal pancreatic cells and were accompanied by greater numbers of lipid droplets (LDs). When evaluating multitask learning techniques utilizing the EfficientNet-b3 model through confusion matrices, the overall 2-category accuracy for cancer classification reached 96.7 %. Simultaneously, the overall 4-category accuracy for individual cell line classification achieved a high accuracy of 96.2 %. Furthermore, when we added the core techniques one by one, the overall performance of the proposed technique significantly improved, reaching an area under the curve (AUC) of 0.997 and an accuracy of 97.06 %. Finally, the AUC reached 0.998 through the ablation study with the score fusion technique. DiscussionOur novel training strategy has significant potential for automating and promoting rapid recognition of pancreatic cancer cells. In the near future, deep learning-embedded medical devices will substitute laborious manual cytopathologic examinations for sustainable economic potential.

Comparison Between Neutron Bragg Dip and Electron Backscatter Diffraction Images of TlBr Semiconductors

Comparison Between Neutron Bragg Dip and Electron Backscatter Diffraction Images of TlBr Semiconductors