Evidence map›Paper›PMID 39207731›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2024

Hierarchy of topological transitions in a network liquid.

Andreas Neophytou, Francis W Starr, Dwaipayan Chakrabarti, Francesco Sciortino

Abstract read
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Hierarchy of topological transitions in a network liquid.Proceedings of the National Academy of Sciences of the United States of America · 2024
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Andreas NeophytouDipartimento di Fisica, Sapienza Universitá di Roma, Roma 00185, Italy.
Francis W StarrDepartment of Physics, Wesleyan University, Middletown, CT 06459.
Dwaipayan ChakrabartiSchool of Chemistry, University of Birmingham, Birmingham B15 2TT, United Kingdom.ORCID 0000-0002-2939-2808
Francesco SciortinoDipartimento di Fisica, Sapienza Universitá di Roma, Roma 00185, Italy.ORCID 0000-0002-2418-2713

Funding

Ministero dell"Istruzione, dell"Università e della Ricerca (MIUR) 2022JWAF7YRoyal Society (The Royal Society) IES\R3\183166
6 · The paper itself

Abstract

The representation of complex systems as networks has become a critical tool across many fields of science. In the context of physical networks, such as biological neural networks, vascular networks, or network liquids where the nodes and edges occupy volume in three-dimensional space, the question of how they become densely packed is of special importance. Here, we investigate a model network liquid, which is known to densify via two successive liquid-liquid phase transitions (LLPTs). We elucidate the importance of rings-cyclic paths formed by bonded particles in the networks-and their spatial disposition in understanding the structural changes that underpin the increase in density across the LLPTs. Our analyses demonstrate that the densification of these networks is primarily driven by the formation of linked rings, and the LLPTs correspond to a hierarchy of topological transitions where rings form the fundamental building blocks. We envisage entanglement to emerge as a general mechanism for densification, with wide implications for the embedding of physical networks, especially in confined spaces.

Indexed as

DNA nanotechnologyliquid–liquid phase transitionnetworksself-assemblytopology

Identifiers

PMID39207731
PMCPMC11388341

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.