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regression CNN Nature paper figure 1

Work on regression CNN that performs full profile analysis of powder diffraction data published in new paper

PhD candidate Hongyang Dong and Finden research scientists have developed a regression CNN that performs full profile analysis of powder diffraction data yielding physicochemical information (scale factors, lattice parameters and crystallite size) from multiphase systems. This project was performed in collaboration with National Physical Laboratory, STFC Scientific Machine Learning Group and UCL Department of Chemistry.

The work has resulted in a paper “A deep convolutional neural network for real-time full profile analysis of big powder diffraction data” published in NPJ Computational Materials 7, 74 (2021).

We present Parameter Quantification Network (PQ-Net), a regression deep convolutional neural network providing quantitative analysis of powder X-ray diffraction patterns from multi-phase systems. The network is tested against simulated and experimental datasets of increasing complexity with the last one being an X-ray diffraction computed tomography dataset of a multi-phase Ni-Pd/CeO2-ZrO2/Al2O3 catalytic material system consisting of ca. 20,000 diffraction patterns. It is shown that the network predicts accurate scale factor, lattice parameter and crystallite size maps for all phases, which are comparable to those obtained through full profile analysis using the Rietveld method, also providing a reliable uncertainty measure on the results. The main advantage of PQ-Net is its ability to yield these results orders of magnitude faster showing its potential as a tool for real-time diffraction data analysis during in situ/operando experiments.

You can read the full paper at https://doi.org/10.1038/s41524-021-00542-4

Figure 2. Spatial distribution of normalised scale factor (in respect to maximum value in this XRD-CT image), spatial distribution of crystallite size and lattice parameter a for CeO2 and ZrO2 collected at room temperature fresh catalyst

Work on 5D chemical imaging of an operating catalyst published

You can see the latest work from our former PhD student Dr Dorota Matras and our scientists on 5D chemical imaging in a new paper published at the Journal of Materials Chemistry A of the Royal Society of Chemistry (RSC): “Multi-length Scale 5D Diffraction Imaging of Ni-Pd/CeO2-ZrO2/Al2O3 Catalyst during Partial Oxidation of Methane.
The work was performed at ESRF in collaboration with UCL Chemistry, VITO and Boreskov Institute of Catalysis.

A 5D diffraction imaging experiment (with 3D spatial, 1D time/imposed operating conditions and 1D scattering signal) was performed with a Ni-Pd/CeO2-ZrO2/Al2O3 catalyst. The catalyst was investigated during both activation and partial oxidation of methane (POX). The spatio-temporal resolved diffraction data allowed us to obtain unprecedented insight into the behaviour and fate of the various metal and metal oxide species and how this is affected by the heterogeneity across catalyst particles. We show firstly, how Pd promotion although facilitating Ni reduction, over time leads to formation of unstable Ni-Pd metallic alloy, rendering the impact of Pd beyond the initial reduction less important. Furthermore, in the core of the particles, where the metallic Ni is primarily supported on Al2O3, poor resistance towards coke deposition was observed. We identified that this preceded via the formation of an active yet metastable interstitial solid solution of Ni-C and led to the exclusive formation of graphitic carbon, the only polymorph of coke observed. In contrast, at the outermost part of the catalyst particle, where Ni is predominantly supported on CeO2-ZrO2, the graphite formation was mitigated but sintering of Ni crystallites was more severe.

Read the full article at https://doi.org/10.1039/D1TA01464A.

 

Dr. Antony Vamvakeros webinar on X-ray diffraction computed tomography July 21 – available to watch online

Antony Vamvakeros webinar promo imageSynchrotron X-ray diffraction computed tomography (XRD-CT) is a marriage between powder diffraction and computed tomography using a “pencil” beam approach. The spatially-resolved signals obtained with XRD-CT can reveal information that would otherwise be lost in bulk measurements, which opens up new possibilities in functional material characterization.

In this webinar, our research scientist Dr. Antony Vamvakeros presented results from key case studies where he and the team applied XRD-CT to track the evolving solid-state chemistry of complex functional materials and devices under operating conditions. The webinar also focussed on the recent technical advances in data acquisition, treatment and handling strategies, as well as bottlenecks/limitations of the technique and the potential routes to overcome them.

For more information and to watch the webinar visit – https://www.dectris.com/landing-pages/dectris-application-webinar-series-2020/

Our latest work using multi-length scale chemical tomography to study fixed bed reactors during the oxidative coupling of methane reaction

Graphical abstractOur latest work using multi-length scale chemical tomography to study fixed bed reactors during the oxidative coupling of methane reaction has just been published at the Journal of Catalysis. “Real-time multi-length scale chemical tomography of fixed bed reactors during the oxidative coupling of methane reaction” is a result of a collaboration between scientists at UCL, Finden, ESRF, Diamond Light Source, Research Complex at Harwell, ISIS Neutron and Muon Source, University of Manchester, Boreskov Institute of Catalysis SB RAS and VITO.

In this work, we present the results from multi-length-scale studies of a Mn-Na-W/SiO2 and a La-promoted Mn-Na-W/SiO2 catalyst during the oxidative coupling of methane reaction. The catalysts were investigated from the reactor level (mm scale) down to the single catalyst particle level (μm scale) with different synchrotron X-ray chemical computed tomography techniques (multi-modal chemical CT experiments). These operando spatially-resolved studies performed with XRD-CT (catalytic reactor) and multi-modal μ-XRF/XRD/absorption CT (single catalyst particle) revealed the multiple roles of the La promoter and how it provides the enhancement in catalyst performance. It is also shown that non-crystalline Mn species are part of the active catalyst component rather than crystalline Mn2O3/Mn7SiO12 or MnWO4.

The paper can be found in Journal of Catalysis, Volume 386, June 2020, Pages 39-52. DOI: https://doi.org/10.1016/j.jcat.2020.03.027

Operando and Postreaction Diffraction Imaging of the La–Sr/CaO Catalyst in the Oxidative Coupling of Methane Reaction

Operando and Postreaction Diffraction Imaging figureOur paper on Operando and Postreaction Diffraction Imaging of the La–Sr/CaO Catalyst in the Oxidative Coupling of Methane Reaction has been published online by The Journal of Physical Chemistry.

A La–Sr/CaO catalyst was studied operando during the oxidative coupling of methane (OCM) reaction using the X-ray diffraction computed tomography technique. Full-pattern Rietveld analysis was performed in order to track the evolving solid-state chemistry during the temperature ramp, OCM reaction, as well as after cooling to room temperature. We observed a uniform distribution of the catalyst main components: La2O3, CaO–SrO mixed oxide, and the high-temperature rhombohedral polymorph of SrCO3. These were stable initially in the reaction; however, doubling the gas hourly space velocity resulted in the decomposition of SrCO3 to SrO, which subsequently led to the formation of a second CaO–SrO mixed oxide. These two mixed CaO–SrO oxides differed in terms of the extent of Sr incorporation into their unit cell. By applying Vegard’s law during the Rietveld refinement, it was possible to create maps showing the spatial variation of Sr occupancy in the mixed CaO–SrO oxides. The formation of the Sr-doped CaO species is expected to have an important role in this system through the enhancement of the lattice oxygen diffusion as well as increased catalyst basicity.

Read the full article at https://pubs.acs.org/doi/10.1021/acs.jpcc.8b09018

5D operando tomographic diffraction imaging of a catalyst bed

Our paper on 5D operando tomographic diffraction imaging of a catalyst bed has been published online by Nature Communications, volume 9, Article number: 4751 (2018)

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. This five-dimensional (three spatial, one scattering and one dimension to denote time/imposed state) approach enabled us to track the chemical evolution of many particles across the catalyst bed and relate these changes to the gas environment that the particles experience. Rietveld analysis of some 2 × 106 diffraction patterns allowed us to extract heterogeneities in the catalyst from the Å to the nm and to the μm scale (3D maps corresponding to unit cell lattice parameters, crystallite sizes and phase distribution maps respectively) under different chemical environments. We are able to capture the evolution of the Ni-containing species and gain a more complete insight into the multiple roles of the CeO2-ZrO2 promoters and the reasons behind the partial deactivation of the catalyst during partial oxidation of methane.

Read the full article at https://doi.org/10.1038/s41467-018-07046-8