Jack Carr

What inspired you to pursue this project and join the DAMC CDT?  

I previously completed an MChem degree at the University of Liverpool, with my final year project researching crystalline inorganic materials with complex thermal properties. I thoroughly enjoyed the more hands on and independent aspects of academic research, which inspired me to stay within the same research group and pursue a PhD so I could continue my work on the discovery of new interesting materials. I also wanted to do a PhD to push myself as a PhD student develops so many important skills like critical thinking, time management, organisation, communication, and problem solving which are useful for any future career. In particular, the CDT was attractive to me as I’ve always had an interest in other related disciplines like computer science and automation but never had the experience, so the opportunities provided by the CDT seemed perfect for me to finally gain this and aid in my project.   

What is your research project about, and what impact do you hope it will have?     

My project focuses on using a synthetic workflow that contains both manual and automated steps in order to increase throughput of samples and the discovery of new materials. Currently the materials I am focusing on are insoluble inorganic metal oxide precursors, with the hopes of finding new phases that are acid-stable for use as water splitting catalysts. My work will contribute to accelerating the process of discovering complex functional inorganic materials for net zero applications, and will provide a route for screening the large chemical space available.  

What has been the most exciting or rewarding part of your PhD journey so far? How does your project benefit from being part of an interdisciplinary CDT like DAMC?  

The best part of my PhD so far has been being able to decide the direction of my project and choosing the materials I want to investigate so I can think more like an independent researcher. My work also benefits from being part of a CDT as I can collaborate with other students on different projects. For example, sending them the large amount of experimental data I produce to test and train their computational models and send me back the results.