The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Models of anomalous diffusion in crowded environments
Abstract
A particle's motion in crowded environments often exhibits anomalous diffusion, whose nature depends on the situation at hand and is formalized within different physical models. Thus, such environments may contain traps, labyrinthine paths or macromolecular structures, which the particles may be attached to. Physical assumptions are translated into mathematical models which often come with nice mathematical instruments for their description, e.g. fractional diffusion equations. The beauty of the instrument sometimes seduces an investigator to use it without any connection to the physical model. The author hopes that the present discussion will reduce the danger of such inappropriate use.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Anomalous diffusion in disordered media: Statistical mechanisms, models and physical applications
Bouchaud, J.-P., & Georges, A. (1990) · Physics Reports · doi:10.1016/0370-1573(90)90099-n · Cited by 4,162
Anomalous Diffusion in Purkinje Cell Dendrites Caused by Spines
Santamaria, F., Wils, S., De Schutter, E., & Augustine, G. J. (2006) · Neuron · doi:10.1016/j.neuron.2006.10.025 · Cited by 279
Cited by
Metzler, R., Jeon, J.-H., Cherstvy, A. G., & Barkai, E. (2014) · Phys. Chem. Chem. Phys. · doi:10.1039/c4cp03465a · Cited by 1,765
Extreme dynamics in a biomolecular condensate
Galvanetto, N., Ivanović, M. T., Chowdhury, A., Sottini, A., Nüesch, M. F., Nettels, D., Best, R. B., & Schuler, B. (2023) · Nature · doi:10.1038/s41586-023-06329-5 · Cited by 365
Metadata
- Authors
- Sokolov, I. M.
- Venue
- Soft Matter
- Publisher
- Royal Society of Chemistry (RSC)
- Published
- 2012
- Volume
- 8
- Issue
- 35
- Pages
- 9043
- ISSN
- 1744-683X (ISSN-L)
- 1744-6848
- OpenAlex
- W1980752513
- Cited by
- 565
- References
- 2
- FWCI
- 40.49
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Models of anomalous diffusion in crowded environments. https://app.cochat.ai/papers/10.1039/c2sm25701g
- The paper
- Sokolov, I. M. (2012). Models of anomalous diffusion in crowded environments. Soft Matter. https://doi.org/10.1039/c2sm25701g
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.