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Address: Building R71, Rutherford Appleton Laboratory, Harwell, Oxford, OX11 0QX
Email: office@finden.co.uk
Telephone: +44 (0)7734 225187
Finden Ltd is a company registered in England and Wales. Company number: 8254352. & VAT number 155119814. Our registered office: Finden, The Oxford Science Park Robert Robinson Avenue, Magdalen Centre, Oxford, England OX4 4GA.
New Battery Imaging Library (BIL)
We are pleased to announce the launch of the Battery Imaging Library (BIL) – www.batteryimaginglibrary.com
BIL is the first open-access collection of multi-modal and multi-length scale battery imaging datasets. A combination of laboratory and synchrotron X-ray CT (micro-CT, nano-CT, XRD-CT), neutron CT, SEM/EDX, EBSD, and S3DXRD. One unique feature is the release of raw experimental data: radiographs, sinograms, X-ray diffraction patterns, and electron Kikuchi diffraction patterns. The library already hosts more than 3.5 TB of compressed data, openly available for the community.
You can find open-access Python notebooks with example workflows at https://github.com/antonyvam/BatteryImagingLibrary
BIL has been designed to support:
• Educators who want to give students hands-on access to real experimental datasets.
• Researchers developing and benchmarking algorithms for reconstruction, denoising, segmentation and other machine learning approaches using experimental industrially relevant data.
• The wider community through FAIR principles, with all datasets permanently archived with DOIs and rich metadata.
BIL is the result of collaboration between multiple institutions and facilities including Imperial College London, Diamond Light Source, ESRF – The European Synchrotron, DESY, μ-VIS X-ray Imaging Centre (muvis), UCL Chemistry, The University of Manchester, ISIS Neutron and Muon Source, UKBIC, National Renewable Energy Laboratory and WMG, University of Warwick. Funding for Dr Antony Vamvakeros’s work was provided by The Royal Society through their Industry Fellowship.
You can find the BIL preprint at https://chemrxiv.org/engage/chemrxiv/article-details/68d3b52b3e708a7649ffd0a5
New paper on nDTomo software in Digital Discovery
We are pleased to have our new paper on nDTomo software from Research & Development Lead Scientist, Dr Antony Vamvakeros accepted in RSC Digital Discovery. The work was done in collaboration with Dyson School of Design Engineering Imperial College London and UCL Chemistry.
nDTomo is a Python-based software suite for the simulation, reconstruction and analysis of X-ray chemical imaging and computed tomography data. It provides a collection of Python function-based tools designed for accessibility and education as well as a graphical user interface (GUI). Prioritising transparency and ease of learning, nDTomo adopts a function-centric design that facilitates straightforward understanding and extension of core workflows, from phantom generation and pencil-beam tomography simulation to sinogram correction, tomographic reconstruction and peak fitting. While many scientific toolkits embrace object-oriented design for modularity and scalability, nDTomo instead emphasises pedagogical clarity, making it especially suitable for students and researchers entering the chemical imaging and tomography field. The suite also includes modern deep learning tools, such as a self-supervised neural network for peak analysis (PeakFitCNN) and a GPU-based direct least squares reconstruction (DLSR) approach for simultaneous tomographic reconstruction and parameter estimation. Rather than aiming to replace established tomography frameworks, nDTomo serves as an open, function-oriented environment for training, prototyping, and research in chemical imaging and tomography.
Read the paper at https://pubs.rsc.org/en/Content/ArticleLanding/2025/DD/D5DD00252D
You can find nDTomo available on PyPI.
More information at:
📚 Docs: https://ndtomo.readthedocs.io/en/master/
⭐ GitHub: https://github.com/antonyvam/nDTomo
Dr Antony Vamvakeros’s work was supported by The Royal Society through the Industry Fellowship.
Methane splitting to hydrogen and base growth carbon nanotubes over Fe-based catalysts
Image credit: Ilenia Giarnieri, Università di Bologna
We were proud to work on a new publication for the Storming EU Project- “Methane splitting to hydrogen and base growth carbon nanotubes over Fe-based catalysts”.
The work highlights:
Our PhD student, Antonia Bobitan, Managing Director, Simon Jaques and Chief Scientific Officer, Andrew Beale collaborated with authors Ilenia Giarnieri, Vito Foderà, Esteban Gioria, Lucy Costley-Wood, Andrea Bertuzzi, Francesca Ospitali, Giuseppe Fornasari, Christian Danvad Damsgaard, M. Clelia Righi and Patricia Benito Martin. The institutions involved were Università di Bologna, University College London, Research Complex at Harwell and Technical University of Denmark.
Read the full article at https://doi.org/10.1016/j.
Information on the Storming EU project can be found at storming-project.eu
This work has received funding from the European Union’s Horizon Europe Research and Innovation Programme, under Grant Agreement n° 101069690 (STORMING). Finden is supported through the Innovate UK / UKRI Horizon Europe Guarantee Scheme.
Join us at EuropaCat 2025
Join us at EuropaCat 2025 from 31 August to 5 September. Our scientists Prof. Andrew Beale, Dr. Steve Price, Dr. Yaro Odarchenko and PhD student Antonia Diana Bobitan will be at our booth to answer any questions you may have on how we can employ advanced catalyst characterisation and analysis to understand why catalysts or processes work or fail and much more …
Information on EuropaCat at https://www.ntnu.edu/web/europacat2025/europacat2025
New nDTomo software from our Research & Development Lead Scientist Dr Antony Vamvakeros
We are pleased to announce the launch of new nDTomo software from our Research & Development Lead Scientist, Dr Antony Vamvakeros. You can find nDTomo available on PyPI.
nDTomo is an open-source Python software suite for simulation, visualisation, reconstruction, and analysis of chemical imaging and X-ray tomography data. It is especially useful for hyperspectral datasets like XRD-CT.
This software has been seven years in the making. Antony started the project during his postdoctoral at the ESRF – The European Synchrotron building GUIs for handling XRD-CT data at beamline ID15A. Over the years he has developed nDTomo to be far from a GUI – now it’s an integrated platform for researchers and students in materials science, catalysis, batteries, and synchrotron applications.
nDTomo features include:
🔍 Interactive visualization of chemical tomography data via the nDTomoGUI
🧪 Generation of multi-dimensional synthetic phantoms
🎯 Simulation of pencil beam CT acquisition strategies
🧼 Pre-processing and correction of sinograms
🛠️ CT image reconstruction using algorithms like filtered back-projection and SIRT
🧠 Dimensionality reduction and clustering for unsupervised chemical phase analysis
📈 Pixel-wise peak fitting using Gaussian, Lorentzian, and Pseudo-Voigt models
🤖 Peak fitting using the self-supervised PeakFitCNN
🔄 Simultaneous peak fitting and tomographic reconstruction using the DLSR approach with PyTorch GPU acceleration
Antony worked on this software alongside Finden colleagues; Dr Evangelos Papoutsellis and Dr Hongyang Dong.
nDTomo is a helpful new tool if you are working with XRD-CT, chemical tomography, or hyperspectral imaging, so try it for yourself.
More information at:
📚 Docs: https://ndtomo.readthedocs.io/en/master/
⭐ GitHub: https://github.com/antonyvam/nDTomo
The new software release includes:
• 📚 Full API documentation + 10 Jupyter notebooks, including recent work we have done at the TLDR group at Dyson School of Design Engineering with Ronan Docherty and Prof Sam Cooper on a self-supervised neural network for peak fitting
• 🧠 Transition from tensorflow to PyTorch for all neural-network and GPU-based tools (e.g. PeakFitCNN, DLSR)
• 🧪 Major GUI upgrades, including XRD-CT phantom generator + Embedded IPython console
• 🧹 Refactored, cleaned, and simplified codebase
• 🚀 First stable release to PyPI
Operando XRD mapping experiment at ESRF for the FutureCat project
Image credit: Harry Gillions (WMG)
Our Senior Scientist Dr. Stephen Price, with Dr. Ashok Menon and Harry Gillions from collaborator institute WMG, University of Warwick completed a beamtime in May at ESRF. They set up an operando XRD mapping experiment on multilayer pouch cells fabricated at the WMG battery scale up pilot line to understand their degradation modes.
Stephen has been studying X-ray diffraction computed tomography (XRD-CT) and mapping techniques to provide direct quantification of performance of real world battery operation without the need to extrapolate from model cells. He is looking to devise non-destructive techniques to rapidly benchmark industry-relevant large-format cells, such as those manufactured at WMG, and further develop the operando methods that can be applied to study battery degradation that occur with long-term cycling. These findings can be translated back to WMG , and then to the broader industry to build better batteries.
Finden partnered with University of Sheffield and WMG as part of FutureCat Phase II activities, led by Prof Louis Piper at WMG to work on the Faraday Institution’s collaborative project along with eight other industrial partners, eight other academic partners and two national research facilities. They aim to create the future lithium-ion cathode materials for electric vehicles.
Latest work exploring the use of the ProxSkip algorithm
We are excited to share our latest work exploring the use of the ProxSkip algorithm as an efficient solution for accelerating iterative methods in imaging inverse problems. This project was led by our Senior Research Scientist Evangelos Papoutsellis, in collaboration with Kostas Papafitsoros (Queen Mary University) and Zeljko Kereta (University College London). By randomly skipping regularisation steps, ProxSkip significantly reduces computational time without compromising convergence. We also introduce a novel variant, PDHGSkip, which further enhances performance. Extensive numerical experiments demonstrate that these methods deliver faster computations while maintaining high-quality reconstructions.
We acknowledge funding from from the Analysis for Innovators (A4i) Denoising of chemical imaging and tomography data project, in collaboration with National Physical Laboratory which supported early development. As part of this effort, we also extended the stochastic optimisation framework in the Core Imaging Library (CIL) to incorporate these new algorithms. We are pleased to announce that this work has been accepted for presentation at the 10th International Conference on Scale Space and Variational Methods in Computer Vision (SSVM2025). For more information, we refer to the preprint version https://arxiv.org/abs/2411.
PhD student Antonia Bobitan completed 2nd beamtime of year for STORMING at DESY
Our PhD student Antonia Bobitan recently completed her 2nd beamtime of the year for STORMING. This time on beamline P21.2 at DESY, where she performed an in situ XRD-CT experiment on 3D-printed monoliths along with collaborators Dr. Matthew Potter (Bath), Maciej Walerowski (Southampton) and Dr. Lucy Costley-Wood (UCL). The team successfully tested our newly designed reactor and generated lots of nice images.
The 3D printed catalysts were produced by project partner VITO, led by Vesna Middelkoop using catalyst material from partner UNIBO led by Patricia Benitio Martin who also leads the consortium. The STORMING project has received funding from the European Union’s Horizon Europe Research and Innovation Programme, under grant agreement no. 101069690.
Successful beamtime for the STORMING project at the ESRF Beamline BM31 in Grenoble, France
Our PhD student Antonia Bobitan took part in a beamtime on beamline BM31 at ESRF in March with Prof. Andrew Beale and Dr. Yaroslav Odarchenko. They joined forces with Maria Asuncion Molina Esquinas from UCL. Antonia performed a combined XAFS/XRD operando experiment on our STORMING catalysts for CH4 conversion to high-purity H2 and CNTs. She is looking forward to spending the next few months analysing the large amount of data this successful experiment generated!
Spatial and Temporal Visualization of Polymorphic Transformations in Pharmaceutical Tablets
Our Senior Scientist Stephen Price has been working with Julia Gasol Cardona, Iain Oswald (University of Strathclyde), Daniel Markl (CMAC), Andy Maloney (CCDC – The Cambridge Crystallographic Data Centre), and teams at DESY, ELDICO Scientific AG and ESRF – The European Synchrotron on a project where X-ray Diffraction Computed Tomography (XRD-CT) has been used to reveal pressure-induced phase transformations in pharmaceutical tablets of glycolide as a function of compaction pressure. This novel application of the XRD-CT methodology enables non-destructive molecular level insight and is a step change in the way we are able to view formulated products. In the broader context of pharmaceutical research, the application of this methodology to pharmaceutically relevant systems will enable a deeper understanding of the effect of tableting pressure on the formulation in pharmaceutical products. Read the article at https://doi.org/10.1002/anie.202412976