Computational design of shape memory polymer nanocomposites

Sliozberg, Yelena R.; Kröger, Martin; Henry, Todd C.; Datta, S. K.; Lawrence, Bradley D.; Hall, Asha; Chattopadhyay, Aditi

doi:10.1016/j.polymer.2021.123476

Cited by 11 publications

(3 citation statements)

References 35 publications

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“…Therefore, chemical modification in the molecular chain and the inclusion of functional additives can improve printability. Thus, the 3D-printed components can be multifunctional in different applications [223][224][225].…”

Section: Nanoparticle-modified Printable Liquid-based Resinmentioning

confidence: 99%

Liquid-Based 4D Printing of Shape Memory Nanocomposites: A Review

Alsaadi

Hinchy

McCarthy

et al. 2023

JMMP

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Significant advances have been made in recent years in the materials development of liquid-based 4D printing. Nevertheless, employing additive materials such as nanoparticles for enhancing printability and shape memory characteristics is still challenging. Herein, we provide an overview of recent developments in liquid-based 4D printing and highlights of novel 4D-printable polymeric resins and their nanocomposite components. Recent advances in additive manufacturing technologies that utilise liquid resins, such as stereolithography, digital light processing, material jetting and direct ink writing, are considered in this review. The effects of nanoparticle inclusion within liquid-based resins on the shape memory and mechanical characteristics of 3D-printed nanocomposite components are comprehensively discussed. Employing various filler-modified mixture resins, such as nanosilica, nanoclay and nanographene, as well as fibrous materials to support various properties of 3D printing components is considered. Overall, this review paper provides an outline of liquid-based 4D-printed nanocomposites in terms of cutting-edge research, including shape memory and mechanical properties.

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Section: Nanoparticle-modified Printable Liquid-based Resinmentioning

confidence: 99%

Liquid-Based 4D Printing of Shape Memory Nanocomposites: A Review

Alsaadi

Hinchy

McCarthy

et al. 2023

JMMP

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“…The importance of capturing thermal response manifests quite differently in the study of shape-memory polymers (SMPs)-a class of intriguing "smart" materials that can be programmed to sustain temporary shape and then recover original shape upon exposure to an appropriate trigger (in this case, temperature). 316 The essential physics of thermoresponsive SMPs based on copolymers revolves around disparities in molecular mobility amongst polymer segments across a transition temperature (glass or melting), such that "rubbery" or fast fluctuating segments allow for deformation while "glassy" or slowly relaxing segments retain memory. From a CG modeling perspective, one must reproduce the rubbery and glassy characteristics on either side of the relevant transition temperature.…”

Section: Stimuli Responsementioning

confidence: 99%

Chemically specific coarse‐graining of polymers: Methods and prospects

Dhamankar

Webb

2021

Journal of Polymer Science

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Coarse-grained (CG) modeling is an invaluable tool for the study of polymers and other soft matter systems due to the span of spatiotemporal scales that typify their physics and behavior. Given continuing advancements in experimental synthesis and characterization of such systems, there is ever greater need to leverage and expand CG capabilities to simulate diverse soft matter systems with chemical specificity. In this review, we discuss essential modeling techniques, bottom-up coarse-graining methodologies, and outstanding challenges for the chemically specific CG modeling of polymer-based systems. This methodologically oriented discussion is complemented by representative literature examples for polymer simulation; we also offer some advisory practical considerations that should be useful for new researchers. Given its growing importance in the modeling and polymer science community, we further highlight some recent applications of machine learning that enhance CG modeling strategies. Overall, this review provides comprehensive discussion of methods and prospects for the chemically specific coarse-graining of polymers.

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“…However, pure shape memory polymers also have certain shortcomings and limitations [18], including lower recovery speed and lower recovery ratio [19]. Therefore, different types of carbon nanoparticles, such as carbon nanotubes [20][21], graphene [22], nanoclay 3 / 32 [23], and metals or metal oxides [24] have been employed to improve shape memory performance [25].…”

Section: Introductionmentioning

confidence: 99%