This PhD project at the University of York explores the fundamental photochemical behavior of β-diketones, focusing on how electrostatic perturbation via alkali metal complexation can disrupt their photochemical landscape. Keto-enol tautomerism is a central mechanism in organic chemistry, and recent research in the group has shown that alkali metal cations can reverse the energy ordering of keto-enol tautomers in molecules like avobenzone, leading to novel photochemical properties. The project aims to systematically study a series of β-diketone complexes using advanced analytical techniques, including IR-laser and UV-laser interfaced mass spectrometry, to measure spectra and investigate tautomer-dependent photochemistry. Computational chemistry using ab initio methods will support the interpretation of experimental data. The research will also address the broader implications of electrostatic tuning for analytical chemistry, particularly in mass spectrometry, where metal ion complexes may affect molecular assignments. The project offers comprehensive training in mass spectrometry, laser spectroscopy, and computational chemistry, with additional opportunities to develop skills in data science, Python programming, and machine learning. Students are encouraged to participate in conferences, international collaborations, and publish in high-profile journals. The Department of Chemistry at York is committed to equality and diversity, holding an Athena SWAN Gold Award and participating in initiatives to widen participation in doctoral study. Funding is available through the Department, EPSRC, or Chemistry Wild Fund, covering tuition, stipend, and research costs for 3 or 3.5 years, depending on the funding source. Applicants should have or expect to achieve at least a UK upper second class degree in Chemistry or a related subject, and meet English language requirements if applicable. The application process includes online submission, possible supervisor contact, and panel interviews for shortlisted candidates. The project is ideal for candidates interested in experimental and computational approaches to physical organic chemistry and instrumental development.

This PhD project at the University of York's Department of Chemistry focuses on developing machine learning (ML) methods to predict the primary photoproducts of aromatic molecules following light-induced chemical changes. The research addresses a major challenge in photochemistry: the difficulty of predicting molecular excited states and their decay pathways, especially the identity of photoproducts, which are crucial in fields such as photocatalysis, organic electronics, and photomedicine. The project will use a combination of experimental and computational approaches. First, a training set of molecule-photoproduct data will be acquired by performing UV laser-LED photodissociation of aromatic molecules using advanced light-interfaced mass spectrometry. Molecules will be mass-selected and isolated as gas-phase ions in an ion-trap, then photon-excited, with photoproducts identified via mass spectrometry. High-throughput measurements will be enabled by LED light sources, ensuring a uniform dataset ideal for ML training. A second training set will be generated from solution-phase photolysis experiments, with mass spectrometry used to identify photoproducts. These datasets will be used to develop ML protocols, employing neural networks and Python-based tools, to link organic molecules with their primary photoproducts and create predictive models for both gas-phase and solution-phase reactions. The project is novel in its use of laser-interfaced mass spectrometry and aims to bridge the current gap in predictive tools for photoproducts, enabling predictions for molecules not previously analyzed and supporting photo-accelerated synthesis. Students will receive comprehensive training in data science, artificial intelligence, mass spectrometry, and experimental techniques, and will be encouraged to participate in conferences and international collaborations. The department supports equality and diversity, with initiatives to increase participation from under-represented groups. Funding covers tuition, stipend, and research costs, with opportunities for both home and international students. Applicants should have or expect to achieve at least a UK upper second class degree in Chemistry or a related subject, and be interested in both experimental and computational research. The application deadline is 6 January 2026, with interviews held in February. For more information, candidates can consult the university and department websites or contact the supervisor.