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Faraday Conference – ECR Themes
16 November 2021
Session: Advanced X-ray and Neutron techniques
Dr Stephen Price is an Industrial Research Fellow with the University of Sheffield, and will be presenting at the ECR day of the Faraday Institution Conference 2021. He will present an overview of the advanced X-ray and analytical techniques Finden has been developing, and how these can be applied to better understand the structural origin of the improved electrochemical performance of the novel cathode materials being developed by the FutureCat project (https://futurecat.ac.uk/), led by Professor Serena Cussen.
This session focuses on the increasingly popular application of X-ray and neutron-based techniques to analyse, image and characterise batteries and their materials. These include both central-facility-based research and analysis that can be performed in university labs to improve the performance and safety of battery technology.
For more information visit https://faradayconference.org.uk/programme/ecr-themes/
Finalists in xTech Global AI Challenge
Our Research Scientist Dr Naomi Omori attended the finalist pitch event, held at Imperial College London Innovation Hub on September 9-10, 2021. The finalists pitched their proposed solutions to a panel of AI experts from DEVCOM Army Research Laboratory (ARL), Air Force AI/ML Accelerator, Naval Information Warfare Center – Pacific (NIWC), DOD Joint AI Center (JAIC), UK Defense Science and Technology Lab (DSTL), and French Defence Innovation Agency (DIA).
Naomi said of the experience, “Being selected as a finalist and representing Finden was a great opportunity. Not only did we get the chance to pitch twice to a lot of useful contacts in the UK and US defence sector, we have also been granted access to an accelerator program that will help Finden to gain traction in this new field. Although we didn’t win any of the cash prizes, our technology pitch was very well received by many of the people I interacted with and it was great to see how the technologies Finden develops can be of interest to sectors beyond what they were originally developed for. I am looking forward to learning more through the xTech accelerator and disseminating useful information to the rest of the Finden team.”
Read more about the competition at https://www.army.mil/article/250419
Latest work on diffraction tomography of a commercially-available cylindrical NMC liion battery
Synchrotron high-energy X-ray diffraction computed tomography has been employed to investigate, for the first time, commercial cylindrical Li-ion batteries electrochemically cycled over the two cycling rates of C/2 and C/20. This technique yields maps of the crystalline components and chemical species as a cross-section of the cell with high spatiotemporal resolution (550 × 550 images with 20 × 20 × 3 µm3 voxel size in ca. 1 h). The recently developed Direct Least-Squares Reconstruction algorithm is used to overcome the well-known parallax problem and led to accurate lattice parameter maps for the device cathode. Chemical heterogeneities are revealed at both electrodes and are attributed to uneven Li and current distributions in the cells. It is shown that this technique has the potential to become an invaluable diagnostic tool for real-world commercial batteries and for their characterization under operating conditions, leading to unique insights into “real” battery degradation mechanisms as they occur.
Read the full paper at https://doi.org/10.1002/smtd.202100512
Work on an industrially-sized PEM fuel cell published
Finden scientists Dr Antony Vamvakeros and Dr Simon Jacques have collaborated with collaborated with a big team from multiple institutions including ESRF, Université Grenoble Alpes and University of British Columbia to investigate an industrially-relevant PEM fuel cell using diffraction tomography. You can see the paper, entitled “Imaging Heterogeneous Electrocatalyst Stability and Decoupling Degradation Mechanisms in Operating Hydrogen Fuel Cells”, published at ACS Energy Letters here: https://pubs.acs.org/doi/10.
The proliferation of hydrogen fuel cell systems is hindered by a degradation of the platinum catalyst. Here, we provide a device-level assessment of the catalyst degradation phenomena and its coupling to nanoscale hydration gradients, using advanced operando X-ray scattering tomography tailored for device-scale imaging. Gradients formed inside the fuel cell produce a heterogeneous degradation of the catalyst nanostructure, which can be linked to the flow field design and water distribution in the cell. Striking differences in catalyst degradation are observed between operating fuel cell devices and the liquid cell routinely used for catalyst stability studies, highlighting the crucial impact of the complex operating environment on the catalyst degradation phenomena. This degradation knowledge gap accentuates the necessity of multimodal, in situ characterization of real devices when assessing the performance and durability of electrocatalysts and, more generally, electrochemically active phases used in energy conversion and storage technologies.
Read the full article at https://doi.org/10.1021/acsenergylett.1c00718
Finden join new network of scientific service providers at MIXN
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
Finden are pleased to be named as external service providers to the TEESMAT platform (https://www.teesmat.eu/about-
Finalists Emerging Technologies Competition 2021
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/
Dr Stephen Price Faraday Institution Industrial Research Fellowship
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
Watch recorded webinar on Chemical Tomography and Neural Networks
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: