Computational Work

Advancing chemical imaging and tomography through new algorithms and machine learning approaches.

Code

nDTomo

A modular Python toolkit for X-ray chemical imaging and tomography (with a focus on hyperspectral modalities such as XRD-CT).

SAMBA

SAMBA is a trainable segmentation tool for materials science that uses deep learning for fast, high-quality labels and random forests for robust, generalizable segmentations.

Vulture

Convolutional upsampling of DINOv2 features for weakly supervised segmentation.

High-Res DINOv2

Upsampling spatialised features from vision transformer (ViT) models like DINOv2 for unsupervised and weakly-supervised materials segmentation.

SD2I

The Single Digit to Image (SD2I) tensorflow-based image reconstruction tool.

ParallaxNet

Eliminate parallax artefacts present in X-ray powder diffraction computed tomography data acquired from large samples.

PQ-Net

A deep convolutional neural network for real-time full profile analysis of big powder diffraction data.

DLSR

A solution to the parallax artefact in X-ray diffraction computed tomography data.

Single-crystal diffraction artefacts

Removing multiple outliers and single-crystal artefacts from X-ray diffraction computed tomography data.

Data

Battery Imaging Library (BIL)

An open, multi-modal, multi-length-scale dataset hub for battery imaging (X-ray CT, XRD-CT, SEM/EDX, EBSD and many more), designed for benchmarking and teaching.

Software

Beamstop

Finden’s first commercial software for chemical imaging and tomography data.

Experimental Work

Multi-scale characterisation of functional materials and devices. Below are some exemplar studies.

Li-ion Batteries

Revealing chemical heterogeneities inside an intact 18650 NCA Li-ion battery during cycling

First dynamic XRD-CT experiment with a cylindrical Li-ion battery

Cycling Rate-Induced Spatially-Resolved Heterogeneities in Commercial Cylindrical Li-Ion Batteries

First XRD-CT study using cylindrical Li-ion batteries

Spatial quantification of dynamic inter and intra particle crystallographic heterogeneities within lithium ion electrodes

Operando high-resolution X-ray diffraction-computed tomography is used to spatially and temporally quantify crystallographic heterogeneities within and between particles throughout both fresh and degraded LixMn2O4 electrodes.

Spatially Resolving Lithiation in Silicon–Graphite Composite Electrodes via in Situ High-Energy X-ray Diffraction Computed Tomography

Combined high-speed XRD and XRD-CT to probe crystallographic heterogeneities within Li-ion electrodes with a spatial resolution of 1 μm.

Heterogeneous Catalysis

Multi-length scale chemical tomography of an operating catalyst bed

Operando spatially-resolved experiments performed with XRD-CT (catalytic reactor) and multi-modal μ-XRF/XRD/absorption CT (single catalyst particle) during the oxidative coupling of methane reaction.

Multi-length scale 5D diffraction imaging of a catalyst bed

Operando XRD spatially-resolved experiment of a catalytic reactor during the partial oxidation of methane reaction.

XRD-CT study of 3D printed commercial and non-commercial catalysts

3D printed Ni/Al2O3 based catalysts for CO2 methanation

Fuel Cells and Membrane Reactors

Investigation of the mechanical robustness/performance of an SOFC during thermal cycling

First dynamic XRD-CT experiment with a micro-tubular solid oxide fuel cell

Imaging Heterogeneous Electrocatalyst Stability and Decoupling Degradation Mechanisms in Operating Hydrogen Fuel Cells

First dynamic XRD-CT experiment with a PEM fuel cell

Operando XRD-CT of catalytic membrane reactors

Investigation of catalytic membrane reactors during the oxidative coupling of methane reaction

Method Development

Five-dimensional Imaging

First 5D operando tomographic diffraction imaging achieved using a continuous rotation-translation approach.

Interlaced X-ray diffraction computed tomography

A data-collection strategy that allows, post experiment, a choice between temporal and spatial resolution.