TY - JOUR
T1 - Soft Matter Roadmap
AU - Barrat, Jean-Louis
AU - Del Gado, Emanuela
AU - Egelhaaf, Stefan U.
AU - Mao, Xiaoming
AU - Dijkstra, Marjolein
AU - Pine, David J.
AU - Kumar, Sanat K
AU - Bishop, Kyle
AU - Gang, Oleg
AU - Obermeyer, Allie
AU - Papadakis, Christine M
AU - Tsitsilianis, Costantinos
AU - Smalyukh, Ivan I
AU - Hourlier-Fargette, Aurelie
AU - Andrieux, Sebastien
AU - Drenckhan, Wiebke
AU - Wagner, Norman
AU - Murphy, Ryan P.
AU - Weeks, Eric R.
AU - Cerbino, Roberto
AU - Han, Yilong
AU - Cipelletti, Luca
AU - Ramos, Laurence
AU - Poon, Wilson C K
AU - Richards, James A.
AU - Cohen, Itai
AU - Furst, Eric M.
AU - Nelson, Alshakim
AU - Craig, Stephen L
AU - Ganapathy, Rajesh
AU - Sood, Ajay Kumar
AU - Sciortino, Francesco
AU - Mungan, M
AU - Sastry, Srikanth
AU - Scheibner, Colin
AU - Fruchart, Michel
AU - Vitelli, Vincenzo
AU - Ridout, S.A.
AU - Stern, M.
AU - Tah, I.
AU - Zhang, G.
AU - Liu, Andrea J
AU - Osuji, Chinedum O.
AU - Xu, Yuan
AU - Shewan, Heather M.
AU - Stokes, Jason
AU - Merkel, Matthias
AU - Ronceray, Pierre
AU - Rupprecht, Jean-Francois
AU - Matsarskaia, Olga
AU - Schreiber, Frank
AU - Roosen-Runge, Felix
AU - Aubin-Tam, Marie-Eve
AU - Koenderink, Gijsje
AU - Espinosa-Marzal, Rosa M.
AU - Yus, Joaquin
AU - Kwon, Jiheon
N1 - Funding Information:
R G thanks Jawaharlal Nehru Centre for Advanced Scientific Research and the Department of Science and Technology (DST), Govt. of India (Swarna Jayanti Fellowship Grant and DST-Nanomission Grant) for financial support. R G also thanks members of the Experimental Soft Matter Research group at JNCASR for many discussions over the years. A K S thanks the DST, Govt of India for support under the Year of Science Professorship. A K S acknowledges gratefully the contributions of his group members over the years and Professor Sriram Ramaswamy for most enjoyable collaboration over the last 30 years.
Funding Information:
C M P gratefully acknowledges Deutsche Forschungsgemeinschaft for funding (PA 771/19-1). C M P and C T thank DAAD and IKY for funding within the IKYDA program.
Funding Information:
The Flatiron Institute is a division of the Simons Foundation, which also supported this work through the collaboration ‘Cracking the glass problem’ via Grant No. 454945 (S A R and A J L) and Simons Investigator Grant No. 327939 (A J L). This work was also supported by the U. S. Department of Energy, Office of Science, DE- DE-SC0020963 (A J L), and the National Science Foundation via NSF-DMR-2005749 (M S and I T), and the UPenn MRSEC NSF-DMR-1720530 (G Z).
Funding Information:
This publication is part of the project How cytoskeletal teamwork makes cells strong (with project number VI.C.182.004 of the NWO Talent Programme which is financed by the Dutch Research Council (NWO)), and the project Light-responsive microalgal living materials (ERC starting Grant No. 101042612). Images are created with BioRender.com .
Funding Information:
This material is based upon work supported by the National Science Foundation under Grant No. CBET- 1637991.
Funding Information:
This work was supported by the NSF Center for the Chemistry of Molecularly Optimised Networks (MONET), CHE-2116298. We thank X Mao and S Wang for providing the graphics in figures and , respectively.
Funding Information:
This work was supported by NSF CBET Award Nos. 2010118, 1804963 and 1509308, and an NSF DMR Award No. 1507607
Funding Information:
D J P acknowledges support from the US Army Research Office (Award No. W911NF-17-1-0328) and the US Department of Energy (Award No. DE-SC0007991).
Funding Information:
Financial support from ERC Advanced Grant No. 884902 ‘SoftML’ is acknowledged.
Funding Information:
Contributions to this article have been made possible due to research funded by the Australian Government through the Australian Research Council (ARC) Grants DP180101919 and LP160100239.
Funding Information:
This material is based upon work supported by the National Science Foundation under Grant Nos. CMMI-2035122 and CMMI-1435920, and Convergence RAISE program IOS-1848671.
Funding Information:
S S acknowledges support through the JC Bose Fellowship (JBR/2020/000015) SERB, DST (India). M M was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Projektnummer 398962893, Projektnummer 21150405, and Projektnummer 390685813.
Funding Information:
I acknowledge the support from MIUR PRIN 2017 (Project 2017Z55KCW).
Funding Information:
The authors gratefully acknowledge financial support by the Deutsche Forschungsgemeinschaft (DFG), the German Ministry for Education and Research (BMBF), the Crafoord Foundation as well as various large-scale research facilities (in particular Diamond Light Source, the ISIS Neutron and Muon Source, the European Synchrotron Radiation Facility, the Forschungsreaktor München II, the Julich Center for Neutron Science, the Institut Laue-Langevin and Oak Ridge Neutron Laboratory) for beamtime allocation and excellent on-site support.
Funding Information:
C O O acknowledges financial support from NSF through DMR-1945966 and DMR- 2223705
Funding Information:
M M thanks the Centre Interdisciplinaire de Nanoscience de Marseille (CINaM) for providing office space. M M, P R and J-F R received funding from the «Investissements d’Avenir» French Government program managed by the French National Research Agency (ANR-16-CONV-0001) and from the Excellence Initiative of Aix-Marseille University—A*MIDEX.
Publisher Copyright:
© 2023 The Author(s). Published by IOP Publishing Ltd.
PY - 2023/12/12
Y1 - 2023/12/12
N2 - Soft materials are usually defined as materials made of mesoscopic entities, often self-organized, 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 characterize them, this renders soft matter systems powerful model systems to investigate statistical physics phenomena, many of them relevant 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 properties of soft materials, while computational approaches that also use machine learning tools are playing a progressively major role in many investigations.
This roadmap paper 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 characterization, instrumental, simulation and theoretical methods as well as general concepts.
AB - Soft materials are usually defined as materials made of mesoscopic entities, often self-organized, 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 characterize them, this renders soft matter systems powerful model systems to investigate statistical physics phenomena, many of them relevant 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 properties of soft materials, while computational approaches that also use machine learning tools are playing a progressively major role in many investigations.
This roadmap paper 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 characterization, instrumental, simulation and theoretical methods as well as general concepts.
KW - colloid
KW - complex
KW - materials
KW - matter
KW - polymer
KW - soft
U2 - 10.1088/2515-7639/ad06cc
DO - 10.1088/2515-7639/ad06cc
M3 - Article
SN - 2515-7639
VL - 7
SP - 1
EP - 104
JO - Journal of Physics: Materials
JF - Journal of Physics: Materials
IS - 1
M1 - 012501
ER -