The Cronin Group

Research in the Cronin Group is motivated by the fascination for complex chemical systems, and the desire to construct complex functional molecular architectures that are not based on biologically derived building blocks.


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Lee Cronin Features in "Science on the Edge" podcast

Lee Cronin has featured in the “Science on the Edge” podcast, which describes itself as a “Series of podcasts covers topics as divers as genetic engineering, the CRISPR Cas9 gene editing technology (together with other gene editing tools such as retro-viruses, liposomes etc), stem cell research, neuroscience, food supplements (separating the facts from the marketing jargon) and even a little philosophy”

In the podcast, Prof Cronin discusses his work in relation to abiogenesis - the origin of life.

Listen to the podcast here.

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Prof. Leroy (Lee) Cronin

Prof Leroy (Lee) Cronin
Regius Chair of Chemistry
Cronin Laboratory
School of Chemistry
Joseph Black Building
University of Glasgow
Glasgow G12 8QQ
Tel: +44 141 330 6650
Email: lee.cronin@glasgow.ac.uk

Latest Publications

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452. [Fe15]: a frustrated, centred tetrakis hexahedron

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451. Robotic Stepwise Synthesis of Hetero-Multinuclear Metal Oxo Clusters as Single-Molecule Magnets

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450. Standardization and Control of Grignard Reactions in a Universal Chemical Synthesis Machine using online NMR

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449. A robotic prebiotic chemist probes long term reactions of complexifying mixtures

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448. Identifying molecules as biosignatures with assembly theory and mass spectrometry

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447. A molecular computing approach to solving optimization problems via programmable microdroplet arrays

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446. Influence of the Contact Geometry and Counterions on the Current Flow and Charge Transfer in Polyoxometalate Molecular Junctions: A Density Functional Theory Study

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445. Elucidating the Paramagnetic Interactions of an Inorganic-Organic Hybrid Radical-Functionalized Mn-Anderson Cluster

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444. Automatic Generation of 3D-Printed Reactionware for Chemical Synthesis Digitization using ChemSCAD

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443. Optimization of Formulations Using Robotic Experiments Driven by Machine Learning DoE


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