The DOI matches its Crossref record; 87 of the 87 references it cites were found in a registry or an index.
The statistical physics of active matter: From self-catalytic colloids to living cells
Open accessAbstract
Abstract These lecture notes are designed to provide a brief introduction into the phenomenology of active matter and to present some of the analytical tools used to rationalize the emergent behavior of active systems. Such systems are made of interacting agents able to extract energy stored in the environment to produce sustained directed motion. The local conversion of energy into mechanical work drives the system far from equilibrium, yielding new dynamics and phases. The emerging phenomena can be classified depending on the symmetry of the active particles and on the type of microscopic interactions. We focus here on steric and aligning interactions, as well as interactions driven by shape changes. The models that we present are all inspired by experimental realizations of either synthetic, biomimetic or living systems. Based on minimal ingredients, they are meant to bring a simple and synthetic understanding of the complex phenomenology of active matter.
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Cites
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The Influence of Cell Mechanics, Cell-Cell Interactions, and Proliferation on Epithelial Packing
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Metadata
- Authors
- Fodor, É., & Cristina Marchetti, M.
- Venue
- Physica A: Statistical Mechanics and its Applications
- Publisher
- Elsevier BV
- Published
- 2018-08
- Volume
- 504
- Pages
- 106-120
- ISSN
- 0378-4371 (ISSN-L)
- OpenAlex
- W2752591129
- Cited by
- 267
- References
- 87
- FWCI
- 15.35
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Fodor, É., & Cristina Marchetti, M. (2018). The statistical physics of active matter: From self-catalytic colloids to living cells. Physica A: Statistical Mechanics and its Applications. https://doi.org/10.1016/j.physa.2017.12.137
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
