SNI 2022 conference

Our Director Dr Simon Jacques gave a talk in the Industry, Innovation and Transfer microsymposium at the SNI 2022 conference in Berlin. The conference was attended by over 400 researchers from across Europe who met to discuss their work insynchrotron radiation, neutrons and ion beams. More information about the event can be found at https://www.helmholtz-berlin.de/events/sni-2022/index_en.html

Big Science Business Forum

Our Director Dr Simon Jacques attended the Big Science Business Forum in Granada a business oriented conference which congregates the main European research infrastructures, focused on technology and with the aim to be the main meeting point between research infrastructures and industry. Simon presented at a training session related to analytical services, opportunities and experience. […]

Prof. Andrew Beale receives 2022 Materials Chemistry Division mid-career Award: Peter Day Award!

We are proud to announce our Chief Scientific Officer Prof. Andrew Beale has won a Royal Society of Chemistry award! He is the winner of the 2022 Materials Chemistry Division mid-career Award: Peter Day Award. Andrew was given the award for his work in the development of novel methodologies using bright light sources to identify […]

The 27th North American Catalysis Society Meeting NAM27

Our R&D Lead Scientist Dr Antony Vamvakeros spoke at the 27th North American Catalysis Society Meeting (NAM27) in New York. This biennial meeting is widely recognized as the premier topical catalysis conference for matters related to homogeneous and heterogeneous catalysis, while also including broad coverage of electro-catalysis and photo-catalysis. He presented our work on diffraction, […]

Team Up for Transfer – KFS Transfer Workshop

Our Director Simon Jacques discussed how to increase the societal benefit of synchrotron radiation sources at the KFS Transfer Workshop with over forty attendees in Berlin. Discussions at the workshop focussed on synchrotron achievements, challenges and ideas for future improvements with three sessions on, “Synchrotron Access for Industrial Research”, “Synchrotron/Industry hardware co-developments” and “Synchrotron/Industry methodological […]

Li-ion battery project collaboration with Teesmat

It was exciting to be involved in this TEESMAT scientific case study. We contributed to the design and acquisition of ex situ X-ray scattering/diffraction tomography measurements of industrially-relevant prolonged cycled Li-ion batteries at beamline ID31 of the ESRF. The data handling of the raw data (ca. 2.25 TB) and the analysis of the ca. 6,500,000 […]

Cross-cluster project close meeting

It was good to see everyone and celebrate at the Cross-cluster project close meeting. It was a very successful project on Accelerating neutron tomography with applied deep learning. We have developed two novel approaches using neural networks to denoise and remove angular undersampling artefacts in tomography datasets; we developed and demonstrated them using neutron tomography […]

Join us in the Faraday Institution FUSE2022 summer internship programme in batteries

Join us for an undergraduate summer internship with Faraday in the Chemical Imaging of Batteries. Studying a STEM degree? Wondering what career to pursue? Interested in finding out more about the battery sector? Keen to spend time with a dynamic community of pioneering battery researchers seeking to find solutions to support a fully electric future? […]

New workshop on Neutron Powder Diffraction on Catalytic Materials

Our Chief Scientific Officer Prof. Andrew Beale will be speaking at an afternoon workshop on the topic of Neutron Powder Diffraction on Catalytic Materials on 22 March at 13:30 GMT. The workshop will take place in Building R68, Room CR12, Rutherford Appleton Laboratory, Harwell Science and Innovation Campus, OX11 0QX. Neutron diffraction is a powerful technique for […]

New position for a Machine Learning and Tomography Scientist

We invite applications for a Machine Learning and Tomography Scientist. The position is initially limited to 1 year. As the Machine Learning and Tomography Scientist, you will be responsible for developing algorithms and machine/deep learning models to handle, analyse and denoise X-ray CT, spectroscopy and diffraction/scattering data. For more information and to apply visit – […]