Development of Chemical Imaging
Finden works directly with clients on short commissioned projects, but is also involved in long term research projects with clients and partners from across the world. Examples are given below including a case study.
Further case studies include:
We report the results from the first 5D tomographic diffraction imaging experiment of a complex Ni–Pd/CeO2–ZrO2/Al2O3 catalyst used for methane reforming.
An X-ray diffraction computed tomography data-collection strategy that allows, post experiment, a choice between temporal and spatial resolution is reported.
This paper reports a simple but effective filtering approach to deal with single‐crystal artefacts in X‐ray diffraction computed tomography (XRD‐CT).
Over the past decade XRD-CT has gained a lot of attraction as a materials characterisation technique that allows for spatially resolving the chemistry inside samples in a non-destructive manner. However, one of the limitations of the technique is related to the sample size due to the parallax artefact. The parallax artefact arises from relatively large samples where X-rays, scattered from a scattering angle 2θ, arrive at multiple detector elements. This phenomenon leads to loss of physico-chemical information associated with diffraction peak shape and position (i.e. altering the calculated crystallite size and lattice parameter values, respectively) and is currently the major barrier to investigating samples and devices at the centimetre level (scale-up problem). We have developed a new tomographic reconstruction algorithm, termed Direct Least-Squares Reconstruction (DLSR) algorithm, which solves the parallax artefact problem in XRD-CT.
Over the past decade, advancements in X-ray sources, optics and detector technologies have led to a dramatic increase in the volume and data quality of experimental powder diffraction patterns. These technical advances are beginning to make high-throughput powder diffraction measurements a reality not just at synchrotron facilities but also at the laboratory. It is currently well-accepted that it is the data analysis that is emerging as the bottleneck for measurement science and not the data acquisition and/or the experiment itself. Conventional data analysis methods, such as least-squares minimisation approaches, are not able to keep up with the data collections rates and there is a need for alternative methods which can provide both fast and accurate results. We have developed the Parameter Quantification Network (PQ-Net), a regression deep convolutional neural network providing quantitative analysis of powder X-ray diffraction patterns from multi-phase systems.