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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Nature Chemistry News and Views: 3D-Printed hydrothermal reactionware

The Cronin Group’s work on 3D Printed hydrothermal reaction vessels is featured in Nature Chemistry’s “News and Views” sections for November 2014. The manufacture of the low-cost 3D-Printed chambers is described as a “potentially exciting development for many researchers, including those working on high-temperature crystallizations of MOFs”. Compared to the traditional stainless steel vessels, 3D-Printed reactionware could offer great advantages in terms of cost-effectiveness and flexibility.

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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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