DIAD-Dual Imaging and Diffraction Beamline
I12-JEEP: Joint Engineering, Environmental and Processing
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Franck P.
Vidal
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Shaghayegh
Afshari
,
Sharif
Ahmed
,
Alberto
Albiol
,
Francisco
Albiol
,
Éric
Béchet
,
Alberto Corbí
Bellot
,
Stefan
Bosse
,
Simon
Burkhard
,
Younes
Chahid
,
Cheng-Ying
Chou
,
Robert
Culver
,
Pascal
Desbarats
,
Lewis
Dixon
,
Johan
Friemann
,
Amin
Garbout
,
Marcos
García-Lorenzo
,
Jean-François
Giovannelli
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Ross
Hanna
,
Clémentine
Hatton
,
Audrey
Henry
,
Graham
Kelly
,
Christophe
Leblanc
,
Alberto
Leonardi
,
Jean Michel
Létang
,
Harry
Lipscomb
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Tristan
Manchester
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Bas
Meere
,
Claire
Michelet
,
Simon
Middleburgh
,
Radu P.
Mihail
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Iwan
Mitchell
,
Liam
Perera
,
Martí
Puig
,
Malek
Racy
,
Ali
Rouwane
,
Hervé
Seznec
,
Aaron
Sújar
,
Jenna
Tugwell-Allsup
,
Pierre-Frédéric
Villard
Diamond Proposal Number(s):
[29820]
Open Access
Abstract: gVirtualXray (gVXR) is an open-source framework that relies on the Beer–Lambert law to simulate X-ray images in real time on a graphics processor unit (GPU) using triangular meshes. A wide range of programming languages is supported (C/C++, Python, R, Ruby, Tcl, C#, Java, and GNU Octave). Simulations generated with gVXR have been benchmarked with clinically realistic phantoms (i.e. complex structures and materials) using Monte Carlo (MC) simulations, real radiographs and real digitally reconstructed radiographs (DRRs), and X-ray computed tomography (XCT). It has been used in a wide range of applications, including real-time medical simulators, proposing a new densitometric radiographic modality in clinical imaging, studying noise removal techniques in fluoroscopy, teaching particle physics and X-ray imaging to undergraduate students in engineering, and XCT to masters students, predicting image quality and artifacts in material science, etc. gVXR has also been used to produce a high number of realistic simulated images in optimisation problems and to train machine learning algorithms. This paper presents a comprehensive review of such applications of gVXR.
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Nov 2025
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I13-2-Diamond Manchester Imaging
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Diamond Proposal Number(s):
[22053, 31714]
Abstract: Accurate predictions of the size and morphology of microstructural features, including defects such as porosity, are essential for predicting the performance of engineering components. Although several multiscale approaches exist in the literature, including direct simulations and volume-averaged models, their predictions are limited due to large computational times and relatively low accuracy. This work utilises transfer learning to link the macroscopic field variable distributions to the mesoscale, in order to estimate sub-grid microstructural defects. Specifically, the model parameters are corrected using experimental measurements of sub-grid scale defects. The proposed methodology is illustrated for predicting porosity in an aluminium alloy automotive component produced using high pressure die casting. The model uses a physics-based localised porosity model for combined gas and shrinkage porosity to train an artificial neural network. This trained machine learning model is subsequently re-trained using macroscale field variables and experimental X-ray microtomography porosity measurements from industrial component made using different process conditions. An unseen region of the same component is used for further testing of the performance of the model. The results show good prediction of pore size distribution and location. These results are then used to determine component fatigue life. Thus, a full process-structure-property model is established. The framework has the potential to be applied to a large class of problems involving predictions of microstructural features over entire macroscopic components.
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Nov 2025
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Beatriz
Costa-Gomes
,
Joel
Greer
,
Nikolai
Juraschko
,
James
Parkhurst
,
Jola
Mirecka
,
Marjan
Famili
,
Camila
Rangel-Smith
,
Oliver
Strickson
,
Alan
Lowe
,
Mark
Basham
,
Tom
Burnley
Open Access
Abstract: Ease of access to data, tools and models expedites scientific research. In structural biology there are now numerous open repositories of experimental and simulated data sets. Being able to easily access and utilize these is crucial to allow researchers to make optimal use of their research effort. The tools presented here are useful for collating existing public cryoEM data sets and/or creating new synthetic cryoEM data sets to aid the development of novel data processing and interpretation algorithms. In recent years, structural biology has seen the development of a multitude of machine-learning-based algorithms to aid numerous steps in the processing and reconstruction of experimental data sets and the use of these approaches has become widespread. Developing such techniques in structural biology requires access to large data sets, which can be cumbersome to curate and unwieldy to make use of. In this paper, we present a suite of Python software packages, which we collectively refer to as PERC (profet, EMPIARreader and CAKED). These are designed to reduce the burden which data curation places upon structural biology research. The protein structure fetcher (profet) package allows users to conveniently download and cleave sequences or structures from the Protein Data Bank or AlphaFold databases. EMPIARreader allows lazy loading of Electron Microscopy Public Image Archive data sets in a machine-learning-compatible structure. The Class Aggregator for Key Electron-microscopy Data (CAKED) package is designed to seamlessly facilitate the training of machine-learning models on electron microscopy data, including electron-cryo-microscopy-specific data augmentation and labeling. These packages may be utilized independently or as building blocks in workflows. All are available in open-source repositories and designed to be easily extensible to facilitate more advanced workflows if required.
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Oct 2025
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I13-2-Diamond Manchester Imaging
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Diamond Proposal Number(s):
[31134]
Open Access
Abstract: Growth kinetics and orientation selection play a significant role in microstructure evolution during metal solidification, while gravity-induced convection adds significant complexity to the process. In-situ, time-resolved X-ray imaging of solidifying grain-refined Al–20 wt.% Cu alloy onboard the MASER-13 sounding rocket enabled the study of equiaxed dendrite growth under diffusion-controlled conditions, eliminating the influence of gravity. A machine learning-enabled analytical pipeline was developed to extract and evaluate the spatiotemporal behaviour of a large number of individual dendrites, including their growth characteristics, rotations and interactions. Post-flight synchrotron X-ray computed tomography and electron backscatter diffraction were used to reconstruct the three-dimensional dendrite structure with embedded details of crystallographic orientations. Correlated data analysis confirmed that most dendrites grew along directions parallel to the {100} plane under highly isothermal, diffusion-controlled conditions. However, growth along atypical directions was also observed, even in this simplified regime. The benchmark data revealed variation in dendrite arm evolution, influenced by local grain interactions and crystallographic orientation selection. It is shown that the equiaxed grains have random crystallographic orientations and evidence suggests that these survive from shortly after nucleation in the bulk liquid under microgravity conditions. The data processing protocols demonstrated here highlight the potential of integrating advanced experimental techniques with modern data science approaches to analyse solidification microstructure formation in metallic alloys under terrestrial and microgravity conditions.
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Oct 2025
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I06-Nanoscience (XPEEM)
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Purnima P.
Balakrishnan
,
Hemian
Yi
,
Zi-Jie
Yan
,
Wei
Yuan
,
Andreas
Suter
,
Christopher J.
Jensen
,
Pascal
Manuel
,
Fabio
Orlandi
,
Takayasu
Hanashima
,
Christy J.
Kinane
,
Andrew J.
Caruana
,
Dirk
Backes
,
Padraic
Shafer
,
Brian B.
Maranville
,
Zaher
Salman
,
Thomas
Prokscha
,
Cui-Zu
Chang
,
Alexander J.
Grutter
Diamond Proposal Number(s):
[42224]
Abstract: The search for chiral topological superconductivity in magnetic topological insulator (TI)-FeTe heterostructures is a key frontier in condensed matter physics, with potential applications in topological quantum computing. The combination of ferromagnetism, superconductivity, and topologically nontrivial surface states brings together the key elements required for chiral Majorana physics. In this work, we examine the interplay between magnetism and superconductivity at the interfaces between FeTe and a series of Te-based TI overlayers. In both Te/FeTe and superconducting MnBi2Te4/FeTe, any interfacial suppression of antiferromagnetism must affect at most a few nanometers. On the other hand, (Bi,Sb)2Te3/FeTe layers exhibit near-total suppression of antiferromagnetic ordering. Ferromagnetic Cr𝑥(Bi,Sb)2−𝑥Te3 (CBST)/FeTe bilayers exhibit net magnetization in both CBST and FeTe layers, with evidence of interactions between superconductivity and ferromagnetism. These observations identify magnetic TI/FeTe interfaces as an exceptionally robust platform to realize chiral topological superconductivity.
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Oct 2025
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I04-Macromolecular Crystallography
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Open Access
Abstract: Recent advances in synchrotron technology have shifted the bottleneck in macromolecular crystallography (MX) data collection from sample irradiation to other time-consuming steps, such as sample alignment. This project, being prototyped by the I04 beamline at the Diamond Light Source(a), is developing a high-throughput automated pipeline for precise and efficient sample centering using machine learning. The pipeline incorporates image analysis and leverages the 'murko' software(b), a machine learning model for sample identification and centering, developed by Proxima 2 at the SOLEIL synchrotron(c). Device management is handled using Ophyd, and the orchestration of plans is managed by Bluesky(d), ensuring a robust and flexible system. By integrating this technology with Kubernetes and Docker containers, we ensure portability and scalability for seamless deployment across various synchrotron facilities. This project is also part of a larger project that aims to significantly accelerate MX workflows, enabling faster and more reliable data collection to support diverse research applications using the bluesky/ophyd technology(e). Future endeavors may involve generating machine learning-based tomographic masks of the sample holder and protein crystal, facilitating optimal X-ray centering grid determination and starting angle selection.
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Oct 2025
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I09-Surface and Interface Structural Analysis
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Ye
Wang
,
Manish
Chhowalla
,
Yiru
Zhu
,
Tieyuan
Bian
,
Ziwei J.
Yang
,
Yuanyua
Zhao
,
Han
Yan
,
Yang
Li
,
Yan
Wang
,
Feng
Ding
,
Jun
Yin
Diamond Proposal Number(s):
[35092, 30105, 33391, 32963, 38086]
Open Access
Abstract: Engineering chiral optical and electronic properties of materials is interesting for applications in sensing and quantum information. State-of-the-art chiral optoelectronic devices are mostly based on three-dimensional (3D) and quasi-two-dimensional (2D) materials. Here we demonstrate chiral 2D MoS2 with sub-nanometer thickness via chirality transfer from l-/d-penicillamine (l-/d-PEN). We report a giant molar ellipticity of 108 deg·cm2/dmol in monolayer solid-state films, up to 3 orders of magnitude higher than 3D chiral materials. Phototransistors with chiral 2D MoS2 channels exhibit gate-tunable circularly polarized light detection with responsivity of >102 A/W and anisotropy g-factor of 1.98, close to the theoretical maximum of 2.0. The reduced dimensionality magnifies the chirality transfer efficiency, allowing realization of ultrasensitive detectors for circularly polarized photons.
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Oct 2025
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Terence
Tan
,
Balázs
Bagó
,
Sebastian
Busch
,
Renaud
Duyme
,
Guillaume
Gaisné
,
Alejandra Noemí
González Beltrán
,
Heike
Görzig
,
Giannis
Koumoutsos
,
Rolf
Krahl
,
Paul
Millar
,
Carlo
Minotti
,
Melanie
Nentwich
,
Lajos
Schrettner
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Kirsty
Syder
,
Philippe
Rocca-Serra
,
Susanna-Assunta
Sansone
,
Stephen P.
Collins
Open Access
Abstract: The Photon and Neutron Experimental Techniques (PaNET) ontology was released in 2021 as an ontology for two major European research infrastructure communities. It provides a standardized taxonomy of experimental techniques employed across the photon and neutron scientific domain, and is part of a wider effort to apply the FAIR (findable, accessible, interoperable, reusable) principles within the community. Specifically, it is used to enhance the quality of metadata in photon and neutron data catalogue services. However, PaNET currently relies on a manual definition approach, which is time consuming and incomplete. A new structure of PaNET is proposed to address this by including logical frameworks that enable automatic reasoning as opposed to the manual approach in the original ontology, resulting in over a hundred new technique subclass relationships that are currently missing in PaNET. These new relationships, which are evaluated by the PaNET working group and other domain experts, will improve data catalogue searches by connecting users to more relevant datasets, thereby enhancing data discoverability. In addition, the results of this work serve as a validation mechanism for PaNET, as the very process of building the logical frameworks, as well as any incorrect inferences made by the reasoner, has exposed existing issues within the original ontology.
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Sep 2025
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I10-Beamline for Advanced Dichroism - scattering
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Miming
Cai
,
Shangyuan
Wang
,
Yuelin
Zhang
,
Xiaoqing
Bao
,
Dekun
Shen
,
Jinghua
Ren
,
Lei
Qiu
,
Haiming
Yu
,
Zhenlin
Luo
,
Mathias
Kläui
,
Shilei
Zhang
,
Nicolas
Jaouen
,
Gerrit
Van Der Laan
,
Thorsten
Hesjedal
,
Ka
Shen
,
Jinxing
Zhang
Diamond Proposal Number(s):
[36632]
Abstract: Symmetry engineering is an effective approach for generating emergent phases and quantum phenomena. In magnetic systems, the Dzyaloshinskii-Moriya (DM) interaction is essential for stabilizing chiral spin textures. The symmetry manipulation of DM vectors, described in three dimensions, could provide a strategy toward creating abundant topologically magnetic phases. Here, we have achieved breaking the rotational and mirror symmetries of the three-dimensional DM vectors in a strongly correlated ferromagnet, which were directly measured through the nonreciprocal spin-wave propagations in both in-plane and out-of-plane magnetic field geometries. Combining cryogenic magnetic force microscopy and micromagnetic simulations, we discover a bimeron phase that emerges between the spin spiral and skyrmion phases under an applied magnetic field. Such an artificially manipulated DM interaction is shown to play a critical role in the formation and evolution of the large-area bimeron lattice, a phenomenon that could be realized across a broad range of materials. Our findings demonstrate that symmetry engineering of the DM vectors can be practically achieved through epitaxial strain, paving the way for the creation of diverse spin topologies and the exploration of their emergent functionalities.
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Sep 2025
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Accelerator Physics
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Open Access
Abstract: The slow losses measured by Beam Loss Monitors (BLMs) at synchrotron light source facilities offer useful but indirect insight into the state of the beam. Patterns arise across the set of BLMs depending on the movement of insertion devices, beam current, temperature, humidity, and other contributors. A variety of neural network models were designed and evaluated to model this behaviour under user beam operation to enable anomaly detection and aid fault investigations.
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Sep 2025
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