Research
Connect catalytic materials, Design of Experiments, process modelling, and machine learning to advance resource-efficient industrial chemistry.
Current interests
The same question runs through each strand: how can chemistry learn more from each experiment while using carbon, energy, and materials more intelligently?
Design of Experiments (DoE), response-surface methods, phenomenological modelling, and machine-learning approaches that turn experimental data into interpretable, robust process decisions.
Multifunctional and heterogeneous catalysts for transforming furfural, HMF, and related platform molecules into γ-valerolactone, FDCA, and other higher-value products.
Metal nanoparticles, polymeric stabilisers, mixed oxides, and polymer–inorganic composite materials with tuned acidity, redox behaviour, and surface properties.
Non-reductive carbon dioxide conversion, mineralisation, and the valorisation of alkaline industrial residues as lower-impact constituents for materials and cement.
Collaboration network
Close collaborators and selected institutional relationships reflected in recent joint publications and project work.
Close collaborators
Institutional network
Core collaborations across industrial chemistry, catalyst synthesis, spectroscopic characterisation, modelling, and sustainable process development.
Joint work on Zr-, Nb-, Sn-, and Pt-containing catalysts for furfural upgrading in batch and continuous-flow reactors.
Composite Aquivion/oxide catalyst development, spray freeze-drying, and the cascade conversion of furfural to γ-valerolactone.
Recent joint publications couple catalyst design with mechanistic, spectroscopic, and computational insight into HMF oxidation.
Research impact
The most cited Scopus-indexed articles, refreshed automatically every week.
Subscribe via RSSPublication data and citation counts from Scopus, updated 24 August 2026. Citation counts link to their Scopus records and change over time.