Relative contributions of chain density and topology to the elasticity of two-dimensional polymer networks

Alamé, Ghadeer; Brassart, Laurence

doi:10.1039/c9sm00796b

Cited by 15 publications

(10 citation statements)

References 61 publications

(77 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…average pre-stretch only. This result was originally obtained by James [23] and highlighted in recent works [22,24].…”

Section: Introductionsupporting

confidence: 67%

“…( 27) recovers the familiar affine estimate of rubber elasticity theory: G = nkT [25]. In general however, the valueR 2 depends on density and topology, and 27) is exact, provided that the springs are randomly oriented and obey relation (22).…”

Section: Appendix B: Macroscopic Stress Of Random Networksupporting

confidence: 52%

“…Eq. (22). The softening brought about by the lower pre-stretch partly offsets the increase in free energy arising from increasing the number of chains per unit volume.…”

Section: Elastic Modulusmentioning

confidence: 98%

“…While spring network models have been previously used to study the mechanical behaviour of soft materials (see e.g. [20][21][22] and references therein), to the best of our knowledge they have not been used before to study connectivity defects (including the presence of loops) in 3D networks of freely-jointed chains.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Effect of Topological Defects on the Elasticity of Near-Ideal Polymer Networks

Alamé

Brassart

2020

Journal of Applied Mechanics

Self Cite

View full text Add to dashboard Cite

In recent years, new types of polymer gels have emerged, which have a well-controlled network structure and few topological defects. These so-called near-ideal polymer networks constitute a good model system to revisit the long-standing problem of structure-property relationships in polymer networks, as well as a promising platform for the development of polymer gels with outstanding mechanical properties. In this work, we investigate the relative contributions of network defects (dangling chains and second-order loops) on the stress-stretch response of near-ideal polymer networks using a computational discrete network model. We identify the average chain pre-stretch as a key parameter to capture the effect of network topology on the elastic modulus and maximum extensibility. Proper account of the chain pre-stretch further leads to scaling relations for the elastic properties in terms of topology parameters that differ from classical estimates of rubber elasticity theory. Stress-stretch curves calculated using the discrete network model are also compared to semi-analytical estimates.

show abstract

“…average pre-stretch only. This result was originally obtained by James [23] and highlighted in recent works [22,24].…”

Section: Introductionsupporting

confidence: 67%

Section: Appendix B: Macroscopic Stress Of Random Networksupporting

confidence: 52%

“…Eq. (22). The softening brought about by the lower pre-stretch partly offsets the increase in free energy arising from increasing the number of chains per unit volume.…”

Section: Elastic Modulusmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Effect of Topological Defects on the Elasticity of Near-Ideal Polymer Networks

Alamé

Brassart

2020

Journal of Applied Mechanics

Self Cite

View full text Add to dashboard Cite

show abstract

“…By computing a score function, we show that only low radical concentration and small fraction of difunctional monomers was found to be associated with significant history-dependency. Coincidently the region of the parameter space is often exploited in polymer design to enhance elasticity 37 , 40 – 42 , whereas the region is also poorly tractable with conventional SSA, which has the best performance when concentrations of all species are comparable 43 .…”

Section: Discussionmentioning

confidence: 99%

Coloured random graphs explain the structure and dynamics of cross-linked polymer networks

Schamboeck

Iedema

Kryven

2020

Sci Rep

View full text Add to dashboard Cite

Step-growth and chain-growth are two major families of chemical reactions that result in polymer networks with drastically different physical properties, often referred to as hyper-branched and crosslinked networks. in contrast to step-growth polymerisation, chain-growth forms networks that are history-dependent. Such networks are defined not just by the degree distribution, but also by their entire formation history, which entails a modelling and conceptual challenges. We show that the structure of chain-growth polymer networks corresponds to an edge-coloured random graph with a defined multivariate degree distribution, where the colour labels represent the formation times of chemical bonds. The theory quantifies and explains the gelation in free-radical polymerisation of cross-linked polymers and predicts conditions when history dependance has the most significant effect on the global properties of a polymer network. As such, the edge colouring is identified as the key driver behind the difference in the physical properties of step-growth and chain-growth networks. We expect that this findings will stimulate usage of network science tools for discovery and design of cross-linked polymers. Already in the early days of network science it has been realised that in dynamic networks the entire time trajectory of network formation may reflect on the topological features of the structure that is formed at the end. In other words, that the structure of a dynamic network may have memory of its past. Some examples of evolving network models include Price's (1965) preferential attachment model 1 , the vertex copying model 2 , network optimisation models 3 , and branching simlicial complexes 4,5. Preferential attachment, vertex copying models, and branching simplicial complexes all result in heavy-tailed degree distributions. In this work, we introduce an evolving network model with an arbitrary degree distribution to show that the different effects induced by the two most common polymerisation processes on the resulting materials are provoked by the presence or absence of memory in the underlaying network structures. Step-growth and chain-growth polymerisation are two major families of chemical reactions that result in polymer networks. For step-growth, such polymers include polyethylene (PE), polyvinyl chloride (PVC), polypropylene (PP) and polyacrylates 6 , which have a variety of applications: including paints and adhesives 7,8 , food packaging 9 , biomaterials and medical devices 10,11. Well-known examples of chain-growth polymers are gels that find applications as absorbents for medical, chemical and agricultural purposes 12 , and coatings made by photopolymerisation 13,14. The reason for the difference in the physical and mechanical properties of the step-and chain-growth derived polymers lies in the distinct network structures. Moreover, the structure of chain-growth networks can be manipulated by adjusting polymerisation conditions and species concentrators in order to optimise physical and mechanical properties. ...

show abstract

Dynamic Bioinks to Advance Bioprinting

Morgan

Moroni

Baker

2020

Adv Healthcare Materials

166

153

View full text Add to dashboard Cite

The development of bioinks for bioprinting of cell‐laden constructs remains a challenge for tissue engineering, despite vigorous investigation. Hydrogels to be used as bioinks must fulfill a demanding list of requirements, mainly focused around printability and cell function. Recent advances in the use of supramolecular and dynamic covalent chemistry (DCvC) provide paths forward to develop bioinks. These dynamic hydrogels enable tailorability, higher printing performance, and the creation of more life‐like environments for ultimate tissue maturation. This review focuses on the exploration and benefits of dynamically cross‐linked bioinks for bioprinting, highlighting recent advances, benefits, and challenges in this emerging area. By incorporating internal dynamics, many benefits can be imparted to the material, providing design elements for next generation bioinks.

show abstract

Relative contributions of chain density and topology to the elasticity of two-dimensional polymer networks

Abstract: Discrete networks simulations are conducted to decorrelate the effects of density and topology on the elasticity of near-ideal random networks.

Cited by 15 publications

References 61 publications

Effect of Topological Defects on the Elasticity of Near-Ideal Polymer Networks

Effect of Topological Defects on the Elasticity of Near-Ideal Polymer Networks

Coloured random graphs explain the structure and dynamics of cross-linked polymer networks

Dynamic Bioinks to Advance Bioprinting

Contact Info

Product

Resources

About