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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Cronin Group Work Published in the First Issue of Chemical Science

The paper entitled ‘Following the self assembly of supramolecular MOFs using X-ray crystallography and cryospray mass spectrometry’ by Georg Seeber, Geoffrey J. T. Cooper, Graham N. Newton, Mali H. Rosnes, De-Liang Long, Benson M. Kariuki, Paul Kögerler and Leroy Cronin will appear in the first issue of Chemical Science, the new flagship journal from the Royal Society of Chemistry.

Link to Journal

The manuscript can already be viewed online by following the above link - watch out for the fully animated graphical abstract!

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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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