The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Statistical thermodynamics of random networks
Abstract
Polymeric networks exhibiting high elasticity consist, typically, of linear chains several hundred bonds in length joined at their ends to ϕ-functional junctions (ϕ > 2). For random unconstrained chains of this length, the density distribution of chain vectors r is Gaussian in satisfactory approximation. The chains interpenetrate copiously in the network; the domain described by the set of ϕ junctions that are topological neighbours of a given junction encompasses many (20–100) spatial neighbours. A phantom network is expressly defined as a hypothetical one whose chains may move freely through one another; the chains act exclusively by introducing a force that is proportional to the distance between each pair of junctions so connected. The following results of James & Guth are rederived for a Gaussian phantom network using a simplified version of their procedure: (1) the mean values r̄ of the individual chain vectors are linear functions of the tensor λ of the principal extension ratios specifying the macroscopic strain, (2) fluctuations ∆r ═ r – r̄ about these mean values are Gaussian, and (3) the mean-square fluctuations depend only on the structure of the network and not on the strain. Additionally, we show (4) that the distribution of the average vectors r̄ is Gaussian, and (5) that ≺(∆r)2≻ ═ (2/ϕ) <r2≻0, a result obtained previously by Graessley. It follows from (1) and (3) that the transformation of chain vectors r of the phantom network is not affine in λ, and hence that junctions exchange neighbours with strain. In real networks, the mutual interpenetration of chains pendent at a given junction must obstruct this process of local rearrangement of junctions; the transformation of chain vectors may therefore be more nearly affine in λ, especially when the network is undiluted. The elastic free energy derived for a phantom network of any functionality and degree of imperfection reduces to ∆Ae1 ═ ½ξkT(I1 – 3), where I1 ═ trace (λTλ) is the first invariant of the strain and ξ is the cycle rank of the network. If the fluctuations of junctions in a real network are suppressed for the reasons stated, the elastic free energy is ∆A*e1 = ξ(1 – 2ϕ)–1 ½kT(I1 – 3) – (2ξ/ϕ) (1 – 2/ϕ)–1kT In (V/V0), where V and V0 are the actual and reference volumes, respectively. The expected trend from ∆A*e1 to ∆Ae1 with dilution may account, qualitatively at least, for the effect of dilution on the stress–strain relation. A similar trend with extension may explain the familiar departure of the observed tension–elongation relation from theory.
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
Statistical mechanics of chain molecules
Flory, P. J., & Volkenstein, M. (1969) · Biopolymers · doi:10.1002/bip.1969.360080514 · Cited by 6,427
Self-citationThe Physics of Rubber Elasticity
Treloar, L. R. G., & Montgomery, D. J. (1959) · Physics Today · doi:10.1063/1.3060678 · Cited by 3,787
Cited by
Perspective: Coarse-grained models for biomolecular systems
Noid, W. G. (2013) · The Journal of Chemical Physics · doi:10.1063/1.4818908 · Cited by 893
Global dynamics of proteins: bridging between structure and function
Bahar, I., Lezon, T. R., Yang, L.-W., & Eyal, E. (2010) · Annual Review of Biophysics · doi:10.1146/annurev.biophys.093008.131258 · Cited by 610
Bahar, I., Atilgan, A. R., Demirel, M. C., & Erman, B. (1998) · Physical Review Letters · doi:10.1103/physrevlett.80.2733 · Cited by 441
The tube model theory of rubber elasticity
Edwards, S. F., & Vilgis, T. A. (1988) · Reports on Progress in Physics · doi:10.1088/0034-4885/51/2/003 · Cited by 384
Yang, L.-W., & Bahar, I. (2005) · Structure · doi:10.1016/j.str.2005.03.015 · Cited by 273
Metadata
- Authors
- Flory, P. J.
- Venue
- Proceedings of the Royal Society of London. A. Mathematical and Physical Sciences
- Publisher
- The Royal Society
- Published
- 1976-11-19
- Volume
- 351
- Issue
- 1666
- Pages
- 351-380
- ISSN
- 0080-4630 (ISSN-L)
- 2053-9169
- OpenAlex
- W2102679425
- Cited by
- 632
- References
- 2
- FWCI
- 13.89
- 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: Statistical thermodynamics of random networks. https://app.cochat.ai/papers/10.1098/rspa.1976.0146
- The paper
- Flory, P. J. (1976). Statistical thermodynamics of random networks. Proceedings of the Royal Society of London. A. Mathematical and Physical Sciences. https://doi.org/10.1098/rspa.1976.0146
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.