We present a systematic and improved quantitative methodology for analysing wavelength-resolved neutron transmission spectra in metallic alloys. The approach is demonstrated using data acquired on both the IMAT beamline at ISIS and the RADEN beamline at J-PARC on Super Duplex stainless steel samples partially covered with thin polyethylene layers to mimic small controlled hydrogen concentrations. Initially, we employed Monte Carlo particle transport simulations to assess the effect of forward-scattered neutrons on the recorded transmission signal as a function of sample-to-detector distance and neutron beam size. Guided by this simulation framework, we define an experimental configuration and data processing workflow that enables the reliable quantification of very small transmission differences, reaching the sub-percent regime. An NCrystal -based least-squares fitting routine is developed to resolve multiple microstructural parameters simultaneously, including unit-cell parameters, phase fractions, element composition and extinction-related parameters. The model quantitatively reproduces the experimental transmission spectra and determines polyethylene layer thicknesses with micrometre-level precision. This work establishes a robust microstructure-sensitive framework for quantitative neutron transmission analysis. The approach enables the quantification of trace elements that produce measurable contrasts in neutron transmission, exemplified here for hydrogen in steel with a demonstrated detection limit of 10 wt p.p.m. The presented workflow provides a broadly applicable framework for improving quantitative neutron imaging across diverse materials and applications.
Metal–organic frameworks (MOFs), and in particular zeolitic imidazolate frameworks (ZIFs), are amongst the most well studied materials in modern chemistry. MOFs emerged as an explicit subfield of chemistry in the late 1980s and 1990s. However, much foundational work was accomplished in earlier decades. Here, we detail the structural and gas-adsorption characterization of [Cu(im) 2 ] (im = imidazolate), a ZIF that was widely reported in the early 1960s. This compound exists as several polymorphs. We re-prepared the historic blue polymorph [Cu(im) 2 ]-B, which has a sodalite topology, confirmed its identity by single-crystal X-ray diffraction, and measured its gas-adsorption isotherms. We conclude that the true origin of ZIFs lies with a copper(II) framework discovered more than 60 years ago that has been overlooked by recent literature.
Protein function depends on their conformational flexibility. Complementary to temperature-dependent studies, high-pressure (HP) macromolecular crystallography is well suited to exploring the conformational space of proteins at atomic resolution. We have developed a new HP cell concept for macromolecular X-ray crystallography that overcomes the limitations of current devices. It consists of a single-crystalline sapphire capillary, which is loaded with multiple crystals and pressurized with helium at freely adjustable gas pressures of up to 120 MPa. Studying five different proteins, we generally observed a compression of the protein structures and a decrease in thermal displacement parameters at high pressure. Structurally, observations included the compression of a central tunnel of a biotechnologically relevant enzyme and the stabilization of ligand binding in the membrane protein photosynthetic reaction center of Cereibacter sphaeroides. For two pharmaceutically relevant proteins, the human kinase DRAK2 and SARS-CoV-2 main protease, we identified concerted movements of subdomains. Such knowledge is essential for predictions of protein dynamics and defined conformational states, offering the potential for improved structure-based drug development.
Accurate particle picking remains a critical step in cryo-electron microscopy (cryo-EM) single-particle analysis, and the field has witnessed the development of a broad spectrum of approaches – from classical template-based methods to modern deep learning frameworks – to address this task. Here we present CryoQSeg , a particle segmentation and picking framework that represents a paradigm shift toward leveraging a state-of-the-art visual large model. Built upon Qwen-Image-Edit-2511 as its core backbone, CryoQSeg is fine-tuned through supervised fine-tuning and reinforcement learning on the public CryoPPP benchmark dataset, enabling the generation of high-fidelity, pixel-level particle masks directly from raw micrographs. To translate these masks into precise particle coordinates, we further integrate a hybrid post-processing pipeline that couples Hough circle detection with the segment anything model, forming a seamless end-to-end workflow from raw image input to accurate localization. We evaluate CryoQSeg on five representative benchmark datasets, demonstrating competitive performance across a range of protein types and imaging conditions in comparison to mainstream methods including Topaz and CryoSegNet . Furthermore, we validate the framework on the heterogeneous Crt-SPARTA-gRNA-tDNA complex, where it successfully resolves three distinct oligomeric states – monomer, dimer and tetramer – at high resolutions, despite using a fraction of the micrographs employed in the original study. By bridging modern visual large models with classical and foundation-model-based post-processing strategies, CryoQSeg demonstrates the potential of this emerging paradigm to deliver a robust and automated solution for cryo-EM particle picking, thereby contributing to a solid foundation for high-resolution three-dimensional structural determination of biological macromolecules.
High-resolution crystal structure determination using X-ray, electron or neutron diffraction is often limited by poor crystallinity, small crystallite size and instrumental factors, which cause significant diffraction peak broadening and a consequent loss of information. This challenge is particularly severe for nanocrystalline materials, whose X-ray powder diffraction (XRPD) profiles exhibit extensive peak overlap, thereby hindering the reliable extraction of structural information through conventional crystallographic methods. We present here a novel deep-learning-based approach to increase the probability of determining the structure of nanocrystals. A deep-learning model is trained to transform simulated diffraction patterns, degraded by reduced crystalline order, into optimal profiles representative of ideal perfectly crystalline materials. Instead of attempting direct structure inference, this approach improves data by making them better suited for standard crystallographic analyses. The resulting pipeline is both robust and versatile, demonstrating consistent performance across varying crystallite sizes on simulated profiles. The procedure has also been successfully applied to experimental data. To our knowledge, this is the first application of deep learning to enhance experimental XRPD data, representing a breakthrough in the structural characterization of nanocrystalline materials, specifically by enabling progress in difficult cases where atomic-level structure determination is hindered by poor diffraction data.
