Entanglement statistics of polymers in a lattice tube and unknotting of 4-plats

The Knot Entropy Conjecture states that the exponential growth rate of the number of n -edge lattice polygons with knot-type K is the same as that for unknot polygons. Moreover, the next order growth follows a power law in n with an exponent that increases by one for each prime knot in the knot decomposition of K . We provide the first proof of this conjecture by considering knots and non-split links in tube T ∗ , an ∞ × 2 × 1 sublattice of the simple cubic lattice. We establish upper and lower bounds relating the asymptotics of the number of n -edge polygons with fixed link-type in T ∗ to that of the number of n -edge unknots. For the upper bound, we prove that polygons can be unknotted by braid insertions. For the lower bound, we prove a pattern theorem for unknots using information from exact transfer-matrices. This work provides new knot theory results for 4-plats and new combinatorics results for lattice polygons. Connections to modelling polymers such as DNA in nanochannels are highlighted. • Linked regions in polymers occur in a localized way, like pearls on a string. • A first proof of knot and link localization for polymers in nanochannels. • Counts for fixed link type polygons grow asymptotically like decorated unknots. • Proofs for 4-plat unknotting via braid word insertions. • A first proof of a pattern theorem for unknot self-avoiding polygons.

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Journal
Discrete Applied Mathematics
Published
2025-09-04
DOI
https://doi.org/10.1016/j.dam.2025.08.042
Primary Topic
Topological and Geometric Data Analysis
Type
article
Field-Weighted Citation Impact
0.00

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article

Entanglement statistics of polymers in a lattice tube and unknotting of 4-plats

Jeremy Eng, C E Soteros, Koya Shimokawa, Nicholas R. Beaton et al.
Discrete Applied Mathematics
Topological and Geometric Data Analysis
article

Entanglement statistics of polymers in a lattice tube and unknotting of 4-plats

Jeremy Eng, C E Soteros, Koya Shimokawa, Nicholas R. Beaton, Mahshid Atapour, Kai Ishihara, Mariel Vázquez
article en

Abstract

The Knot Entropy Conjecture states that the exponential growth rate of the number of n -edge lattice polygons with knot-type K is the same as that for unknot polygons. Moreover, the next order growth follows a power law in n with an exponent that increases by one for each prime knot in the knot decomposition of K . We provide the first proof of this conjecture by considering knots and non-split links in tube T ∗ , an ∞ × 2 × 1 sublattice of the simple cubic lattice. We establish upper and lower bounds relating the asymptotics of the number of n -edge polygons with fixed link-type in T ∗ to that of the number of n -edge unknots. For the upper bound, we prove that polygons can be unknotted by braid insertions. For the lower bound, we prove a pattern theorem for unknots using information from exact transfer-matrices. This work provides new knot theory results for 4-plats and new combinatorics results for lattice polygons. Connections to modelling polymers such as DNA in nanochannels are highlighted. • Linked regions in polymers occur in a localized way, like pearls on a string. • A first proof of knot and link localization for polymers in nanochannels. • Counts for fixed link type polygons grow asymptotically like decorated unknots. • Proofs for 4-plat unknotting via braid word insertions. • A first proof of a pattern theorem for unknot self-avoiding polygons.

Discrete Applied MathematicsVol. 379
Hiroshima University (JP), Tokyo University of Science (JP), The University of Melbourne (AU), Capilano University (CA), Ochanomizu University (JP), Saskatoon City Hospital (CA), University of Saskatchewan (CA), University of California, Davis (US)
National Science Foundation, Pacific Institute for the Mathematical Sciences, Compute Canada, Natural Sciences and Engineering Research Council of Canada, Australian Research Council, Japan Society for the Promotion of Science, Division of Mathematical Sciences
Openalex Percentile: Top 75%
Topological and Geometric Data Analysis
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