Soft matter roadmap

Journal Article (2024)
Author(s)

Stefan U. Egelhaaf (Heinrich Heine University)

Xiaoming Mao (University of Michigan)

Marjolijn Dijkstra (Universiteit Utrecht)

David J Pine (City University of New York)

Sanat K. Kumar (Columbia University)

M.E. Aubin-Tam (TU Delft - BN/Marie-Eve Aubin-Tam Lab)

G.H. Koenderink (TU Delft - BN/Gijsje Koenderink Lab)

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Contributor(s)

Jean-Louis Barrat – Guest editor (Université Grenoble Alpes)

Emanuela Del Gado – Guest editor (Georgetown University)

Research Group
BN/Marie-Eve Aubin-Tam Lab
DOI related publication
https://doi.org/10.1088/2515-7639/ad06cc
More Info
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Publication Year
2024
Language
English
Research Group
BN/Marie-Eve Aubin-Tam Lab
Journal title
JPhys Materials
Issue number
1
Volume number
7
Article number
012501
Pages (from-to)
105
Downloads counter
357
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Abstract

Soft materials are usually defined as materials made of mesoscopic entities, often self-organised, sensitive to thermal fluctuations and to weak perturbations. Archetypal examples are colloids, polymers, amphiphiles, liquid crystals, foams. The importance of soft materials in everyday commodity products, as well as in technological applications, is enormous, and controlling or improving their properties is the focus of many efforts. From a fundamental perspective, the possibility of manipulating soft material properties, by tuning interactions between constituents and by applying external perturbations, gives rise to an almost unlimited variety in physical properties. Together with the relative ease to observe and characterise them, this renders soft matter systems powerful model systems to investigate statistical physics phenomena, many of themrelevant as well to hard condensed matter systems. Understanding the emerging properties from mesoscale constituents still poses enormous challenges, which have stimulated a wealth of new experimental approaches, including the synthesis of new systems with, e.g. tailored self-assembling properties, or novel experimental techniques in imaging, scattering or rheology. Theoretical and
numerical methods, and coarse-grained models, have become central to predict physical propertiesof soft materials, while computational approaches that also use machine learning tools are playing a progressively major role in many investigations. This Roadmap intends to give a broad overview of recent and possible future activities in the field of soft materials, with experts covering various developments and challenges in material synthesis and characterisation, instrumental, simulation and theoretical methods as well as general concepts.