High-Throughput Operando Spectroscopy to Reveal What Really Controls Electrocatalyst Performance Across the Periodic Table PhD studentship in Developing Automated In-Situ Spectroscopies to Understand Periodic Trends in Electrocatalyst Performance for Carbon-Neutral Transformations

About the project

About the Project Supervisor: Dr Benjamin Moss Application deadline: December 20 th 2026 Interview date: January 15 th 2027 UCL Chemistry Department is offering a fully funded studentship to a highly motivated candidate to start in February 2027. The student will carry out their doctoral research at UCL. Machine learning has transformed drug discovery, yet a comparable acceleration has not happened for clean-energy materials such as electrocatalysts for carbon-neutral fuels. Billions of dollars are being bet on vast compute and datasets outcompeting physical understanding. This gamble may backfire: the nanoscale interfaces that control catalyst performance often emerge or evolve only during operation, so the parameters that describe them can only be measured operando. Operando spectroscopy, however, is slow, painstaking and incompatible with high-throughput approaches. High throughput operando spectroscopy modules that any lab could use would greatly improve the prospects of AI-driven materials discovery. It would also let us answer a fundamental question for the first time: do the parameters long thought to dominate catalyst performance really control activity in real materials, or are their effects confounded and drowned out by other parameters when many materials are compared at scale? We have recently developed HiSPEC a high-throughput in-situ UV-Vis spectroelectrochemistry platform that measures the binding energies of electrocatalysts. In this project you will develop the second iteration of this instrument HiSPEC-2; automating in-situ UV-Vis and IR spectroelectrochemistry systems, and apply machine learning to extract the physical performance descriptors (e.g. substrate binding energies) that govern how electro- and photocatalysts behave across the periodic table. Measuring these descriptors systematically across many materials will show which of them genuinely predict performance and which are masked by competing effects, while producing the first large, consistent operando dataset to accelerate the discovery of catalysts for renewable hydrogen and carbon-neutral chemicals. You will gain highly sought-after, transferable expertise in automation, spectroscopy, electro-/photochemistry and data science within the ASEM group ( asem-lab.org ). You will be able to shape your own project, with additional supervision from experts in X-ray characterisation and AI, and the potential to travel to the USA, China and Japan. The applicants should have, or be expecting to achieve, a first or upper second-class Honours degree or equivalent in chemistry, physics, chemical engineering, materials science, or digital chemistry. Interested candidates should initially contact the supervisor, Dr Benjamin Moss, with a degree transcript and a motivation letter expressing interest in this project. Informal inquiries are encouraged. Please note that a suitable applicant will be required to complete MS Form entitled Application for Research: degree Chemistry programme. In addition, it is essential that suitable applicants complete an electronic application form at http://www.ucl.ac.uk/prospective-students/graduate/apply (please select Research degree: Chemistry programme) prior to the application deadline and advise their referees to submit their references as soon as possible. All shortlisted applicants will be invited for an interview no more than 4 weeks after the application deadline. Any admissions queries should be directed to Fahmida Yasmin or Dr Jadranka Butorac at doctoral.chem@ucl.ac.uk .

Research areas