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Finden have joined a network of mediator companies at MIXN.
As mediators, Finden will be one of the companies helping industry access product insight by use of x-rays and neutrons. The network work with advanced synchrotron and neutron facilities across Europe, helping customers work within sectors as diverse as pharmaceuticals, energy, and engineering.
We are pleased to join the network in helping customers access these modern techniques for material analysis.
Read more at – https://mixn.org
Finden are pleased to be named as external service providers to the TEESMAT platform (https://www.teesmat.eu/about-
We are thrilled to be nominated as Finalists in the Enabling Technologies category of the Emerging Technologies Competition: 2021. The Emerging Technologies Competition is the Royal Society of Chemistry’s annual initiative for early stage companies and academic entrepreneurs who want to commercialise their technologies to make a societal impact. The Final is coming up on the 29-30 June 2021 and our pitch will be led by our Research Scientist Naomi Omori. Read more at https://www.rsc.org/competitions/emerging-technologies/
We are delighted our Senior Scientist Dr Stephen Price has received a Faraday Institution Industrial Research Fellowship. He will be working with the FutureCat project to discover, develop and deploy the next generation of cathode materials to drive the transition towards electric vehicles. Dr Stephen Price will be working in collaboration with the University of Sheffield on the Pushing Known Structures theme. He will be applying methods including XRD-CT to the new cathodes developed by the FutureCat project. You can read more about FutureCat at https://futurecat.ac.uk
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
Our Research and Development Lead Scientist Antony Vamvakeros gave a webinar on chemical tomography and neural networks on the 20th May hosted by the Neel Institut
Synchrotron X-ray chemical tomography methods combine a scattering or spectroscopic technique with a tomographic data acquisition approach. These non-destructive methods yield a cross-section of the studied sample where each pixel in the reconstructed images corresponds to a chemical signal (e.g. X-ray diffraction pattern or spectrum). These spatially-resolved signals most often reveal information that is lost in conventional bulk measurements. In this talk, he briefly introduces X-ray diffraction computed tomography (XRD-CT) and presents a few examples where we have applied such methods to track the evolving solid-state chemistry of complex functional materials and devices under operating conditions. In the second part of the webinar, he focuses on our latest technical advances regarding processing and analysis of these large and rich chemical imaging datasets using deep learning methods with neural networks.
Watch a recording of the webinar below:
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.
We are very pleased to be featured in ESRF Highlights 2020 – their annual round-up of the most exciting experiments carried out at the ESRF in 2020. Read more on page 164 at http://www.esrf.eu/Apache_files/Highlights/2020/index.html#/page/166
We are looking forward to working with the University of Sheffield on the new Faraday Industry Fellowship, a collaborative energy storage research project.
This is an innovative programme that strengthens ties between battery researchers working in industry and academia.
Each fellowship enables academics and industrialists to undertake a mutually beneficial, electrochemical energy storage research project that aims to solve a critical industrial problem and that has the potential for near- and longer-term benefit to the wider UK battery industry.
We will be working to deepen the understanding of new cathode materials. The aim is to fast track the best-performing high energy density cathodes to aid their early adoption by UK industry
Read all about it at https://faraday.ac.uk/ind-fellowship-feb2021/
A team of scientists from Finden Ltd (Dr Stephen Price, Dr Simon Jacques and Prof Andrew Beale) in collaboration with Infineum UK Ltd (Dr Nathan Hollingsworth, Dr Matthew Irving) have used IMAT to help understand where and how coking occurs on engine components. This understanding will help develop more durable, fuel efficient lubricants.
Read the case study at https://www.isis.stfc.ac.uk/Pages/IMAT-Coking-of-engine-components.aspx