With its ability to enable solvent-free chemical reactions, mechanochemistry promises to open new and greener synthetic routes to chemical products of industrial interest. Its practical exploitation requires understanding the relationships between processing variables, powders’ mechanical behaviour, and chemical reactivity. To this aim, rationalizing experimental kinetics is of paramount importance. In this work, we propose a phenomenological kinetic model that could help experimentalists to disentangle the mechanical, chemical, and statistical factors underlying mechanochemical reactions. The model takes into account the statistical nature of ball milling and relates the global kinetic curve that can be obtained experimentally to the deformation and chemical processes that occur on the mesoscopic and microscopic scales during individual impacts. We show that our model equations can satisfactorily best fit experimental datasets, providing information on the underlying mechanochemistry.

A phenomenological kinetic equation for mechanochemical reactions involving highly deformable molecular solids

Delogu F.
;
Porcheddu A.
2021-01-01

Abstract

With its ability to enable solvent-free chemical reactions, mechanochemistry promises to open new and greener synthetic routes to chemical products of industrial interest. Its practical exploitation requires understanding the relationships between processing variables, powders’ mechanical behaviour, and chemical reactivity. To this aim, rationalizing experimental kinetics is of paramount importance. In this work, we propose a phenomenological kinetic model that could help experimentalists to disentangle the mechanical, chemical, and statistical factors underlying mechanochemical reactions. The model takes into account the statistical nature of ball milling and relates the global kinetic curve that can be obtained experimentally to the deformation and chemical processes that occur on the mesoscopic and microscopic scales during individual impacts. We show that our model equations can satisfactorily best fit experimental datasets, providing information on the underlying mechanochemistry.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11584/331805
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