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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The 81st Joseph Henry Lecture by Lee Cronin

Lee Cronin gives the 81st Joseph Henry Lecture at the Philosophical Society of Washington on the 25th May 2012. This prestigious lecture, given every year since 1931 (except 1943), was given by Lee with the title ‘Synthesizing Life’ in which he described our efforts to explore complex chemical systems beyond ‘organic-biology’ to set up inorganic-chemical-systems capable of Darwinian evolution. Lee was also made a member of the society which is the oldest scientific society in Washington, being established in 1871.

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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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