Exploring spectroscopic X-ray nano-imaging with Zernike phase contrast enhancement

Deep learning to overcome Zernike phase-contrast nanoCT artifacts for automated micro-nano porosity segmentation in bone

Bone material contains a hierarchical network of micro- and nano-cavities and channels, known as the lacuna-canalicular network (LCN), that is thought to play an important role in mechanobiology and turnover. The LCN comprises micrometer-sized lacunae, voids that house osteocytes, and submicrometer-sized canaliculi that connect bone cells. Characterization of this network in three dimensions is crucial for many bone studies. To quantify X-ray Zernike phase-contrast nanotomography data, deep learning is used to isolate and assess porosity in artifact-laden tomographies of zebrafish bones. A technical solution is proposed to overcome the halo and shade-off domains in order to reliably obtain the distribution and morphology of the LCN in the tomographic data. Convolutional neural network (CNN) models are utilized with increasing numbers of images, repeatedly validated by `error loss' and `accuracy' metrics. U-Net and Sensor3D CNN models were trained on data obtained from two different synchrotron Zernike phase-contrast transmission X-ray microscopes, the ANATOMIX beamline at SOLEIL (Paris, France) and the P05 beamline at PETRA III (Hamburg, Germany). The Sensor3D CNN model with a smaller batch size of 32 and a training data size of 70 images showed the best performance (accuracy 0.983 and error loss 0.032). The analysis procedures, validated by comparison with human-identified ground-truth images, correctly identified the voids within the bone matrix. This proposed approach may have further application to classify structures in volumetric images that contain non-linear artifacts that degrade image quality and hinder feature identification.

Propagation-based phase-contrast imaging method for full-field X-ray microscopy using advanced Kirkpatrick-Baez mirrors

We demonstrate a propagation-based phase-contrast imaging method for full-field X-ray microscopy based on advanced Kirkpatrick-Baez (AKB) mirrors to achieve high-contrast observations of weak phase objects and correct field curvature aberrations. Through a demonstration performed at SPring-8, the phase contrast of weak phase objects such as polystyrene spheres and chemically fixed cells was successfully observed with high sensitivity (∼0.03 rad). Furthermore, the field of view of the AKB mirrors was expanded to the full area of the obtained images (25 × 30 µm) by correcting the field curvature aberration using reconstructed complex wavefields.

Solid-State Reaction Heterogeneity During Calcination of Lithium-Ion Battery Cathode

During solid-state calcination, with increasing temperature, materials undergo complex phase transitions with heterogeneous solid-state reactions and mass transport. Precise control of the calcination chemistry is therefore crucial for synthesizing state-of-the-art Ni-rich layered oxides (LiNi_1-x-y Co_x Mn_y O₂ , NRNCM) as cathode materials for lithium-ion batteries. Although the battery performance depends on the chemical heterogeneity during NRNCM calcination, it has not yet been elucidated. Herein, through synchrotron-based X-ray, mass spectrometry microscopy, and structural analyses, it is revealed that the temperature-dependent reaction kinetics, the diffusivity of solid-state lithium sources, and the ambient oxygen control the local chemical compositions of the reaction intermediates within a calcined particle. Additionally, it is found that the variations in the reducing power of the transition metals (i.e., Ni, Co, and Mn) determine the local structures at the nanoscale. The investigation of the reaction mechanism via imaging analysis provides valuable information for tuning the calcination chemistry and developing high-energy/power density lithium-ion batteries.