EUropean TOPology Interdisciplinary Action

EUTOPIA: COST Action CA17139

The physical properties of many systems, ranging from naturally occurring biopolymers to artificial materials, often crucially depend on those global features that cannot be ascribed to a particular geometry or arrangement, rather to a more abstract notion: topology. The latter manifests itself in the knotted state of proteins and artificial polymers, the intertwining among DNA rings, or the topologically distinct classes of defect lines that can be found in liquid crystals. A better understanding of the interplay between a system’s topological state, its three-dimensional structure, and its overall characteristics paves the way to an improved control of relevant natural molecules or human-made materials, with remarkable impact on fundamental science as well as high-tech applications. These goals, however, can only be achieved through a multidisciplinary effort, involving a wide spectrum of expertise in a concerted manner.

The EUTOPIA COST Action will establish a collaborative platform to approach all those problems, in the study of biological and soft matter, that feature topological characteristics. In doing this, it will create a pan-European, synergistic network of researchers from different fields that will overcome geographical, economical and societal barriers, as well as those naturally surrounding traditional academic communities.

The outcomes of the research carried out thanks to the EUTOPIA Action will push forward the boundaries of our current understanding of key systems, and foster the knowledge transfer of scientific findings to industry and, ultimately, to society as a whole.

The Working Groups


Theory of topological entanglement in polymers and fibres.


Polymeric and fibrous topological materials.


Entangled and self-entangled proteins.


DNA, chromosomes, and other entangled genetic material.


Topologically complex fluids.


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Mechanical scission of a knotted polymer
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Continuous Crossover from the Dilute to Semidilute Concentration Regime in Spherically Confined Polymers
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Towards Optimal Production of Graphene by Electrolysis in Molten Salts Using Machine Learning
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