Shape memory behaviour of HA-g-PDLLA nanocomposites prepared via in situ polymerization

Du, Ke; Gan, Zhihua

doi:10.1039/c3tb21861a

Cited by 41 publications

(37 citation statements)

References 42 publications

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“…For many physically crosslinked SMP systems such as thermoplastic polyurethane SMPs, the netpoints are microscopic phases or domains corresponding to the hard segments. Interesting variants are the so-called nanoparticle tethered SMP systems with linear polymer chains grafted onto nanoparticles [37,[42][43][44]. For this type of systems, the nanoparticles alone do not establish the necessary network since the other ends of the grafted chains remain free.…”

mentioning

confidence: 99%

Recent progress in shape memory polymer: New behavior, enabling materials, and mechanistic understanding

Zhao

Xie

2015

Progress in Polymer Science

1,191

749

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Shape memory polymers (SMPs), as a class of programmable stimuliresponsive shape changing polymers, are attracting increasing attention from the standpoint of both fundamental research and technological innovations. Following a brief introduction of the conventional shape memory effect (SME), progress in new shape memory enabling mechanisms and triggering methods, variations of in shape memory forms (shape memory surfaces, hydrogels, and microparticles), new shape memory behavior (multi-SME and two-way-SME), and novel fabrication methods are reviewed. Progress in thermomechanical modeling of SMPs is also presented. Abbreviations:SCPs shape changing polymers; LCEs liquid crystalline elastomers; SMP shape memory polymer; SME shape memory effect; 2W two-way; 1W oneway; T trans transition temperature; T g glass transition temperature; T m melting temperature; T cl liquid crystal cleaning temperature; T d deformation temperature; T f shape fixing temperature; T c crystallization temperature; R f shape stability ratio; R r shape recovery ratio; T sw switching temperature; ε max maximum recoverable strain; σ max maximum recovery stress; SMC shape memory cycle; ε load strain under load; ε fixed strain; T r recovery Page 2 of 106 A c c e p t e d M a n u s c r i p t 2 temperature; ε rec recovered strain; V r strain recovery rate; T σmax temperature corresponding to σ max ; T sw,app apparent switching temperature; PCL poly(ε-caprolactone); SMPU shape memory polyurethane; EMU elemental memory unit; TME temperature memory effect; PU polyurethane; T i liquid-crystal isotropic transition temperature; T v vitrification temperature; CIE crystallization induced elongation; MIC melting induced contraction

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mentioning

confidence: 99%

Recent progress in shape memory polymer: New behavior, enabling materials, and mechanistic understanding

Zhao

Xie

2015

Progress in Polymer Science

1,191

749

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“…However, researchers have recently been adding biocompatible inorganics to polymer networks in order to obtain more biofriendly properties. For example, when researchers added the bone mineral-mimicking hydroxyapatite (HA) to a poly(D,L-lactide) (PDLLA) network, they not only validated a SMPINC with fully explored biocompatibility but also showed a network with significantly increased shape recovery ratios of up to 99.5%, depending on the ratio of HA to PDLLA [99,100]. HA has also been used in a growth factor delivery system, where the HA loading of the chemically crosslinked poly(ε-caprolactone) (c-PCL) was used to create uniform pore sizes in the smart network [101].…”

Section: Enhancing Biocompatibilitymentioning

confidence: 99%

“…From remote actuation [124,138], to electrically conductive networks [42,[139][140][141] and all the way to enhanced compatibility within the biological space [99,100], SMPINCs have demonstrated the value of the addition of inorganic particles to a variety of shape memory polymer networks. In this new space of imparting added functionality to shape memory polymers, researchers are continuing to demonstrate innovation in the creation of smart materials that respond to external stimuli.…”

Section: Conclusion: the Future Of Smpincsmentioning

confidence: 99%

Shape Memory Polymer–Inorganic Hybrid Nanocomposites

Reit

Lund

Voit

2014

Organic-Inorganic Hybrid Nanomaterials

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Shape memory polymers (SMPs) have been the focus of much research over the last few decades. From the novelty of temporarily fixing a threedimensional shape from a planar polymer sheet, to the uses that SMPs are seeing today as softening biomedical implants and self-deploying hinges, this class of smart materials has successfully been used to tackle a variety of biological, electrical, and mechanical problems. However, the properties of these networks are limited by the organic nature of the SMPs. To enhance their properties, researchers across the globe have looked into imparting the desirable properties of inorganic composite materials to these polymer networks. As the field of shape memory polymer composites began to grow, researchers quantified the unique enhancements that came at varying filler loading levels as a result of controlled material interface interactions. Specifically, the incorporation of nanofillers of various shapes and sizes leads to increased internal interfacial area relative to micro-and macrocomposites at identical loading fractions and imparts interesting mechanical, optical, electrical, thermal, and magnetic properties to these emerging nanocomposites. This new class of material, referred to in this review as shape memory polymer-inorganic nanocomposites (SMPINCs), allows a host of new interactions between the smart polymer and its surrounding environment as a result of the ability to control the internal environment of the polymer network and nanofiller. In this work, the reader is introduced to both the methods of preparing these composites and the effects the fillers have on the biological, electromagnetic, and mechanical properties of the resulting composite.

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“…Common technologies widely employed for the preparation of nanostructured systems are in situ polymerization, solvent mixing, and melt‐mixing . However, the last one remains the most convenient for the mass production of nanocomposites given its adaptability to available industrial equipments and, from the environmental point of view, for the lack of solvents.…”

Section: Introductionmentioning

confidence: 99%

Thermo‐mechanical behavior of poly(butylene terephthalate)/silica nanocomposites

Russo

Costantini

Luciani

et al. 2017

J of Applied Polymer Sci

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In this work, experimental results about poly(butylene terephthalate) (PBT) based nanocomposites filled with various amounts of silica nanoparticles (NPs) are reported. Two different types of filler are used: silica gel NPs, produced through the Stöber method, and a commercial fumed silica, both coated by a PBT shell. Melt‐mixed samples have been thoroughly investigated by scanning and transmission electron microscopy, infrared Fourier transform spectroscopy (FTIR), thermal gravimetric analysis, differential scanning calorimetry, wide and small angle X‐ray diffraction, and dynamic mechanical analysis. A fine and very good dispersion of NPs into the polymeric matrix is revealed through the morphological analysis when Stöber NPs were used as filler with respect to systems including commercial fumed silica particles. This evidence, combined with matrix–filler interactions revealed by FTIR spectroscopy, justifies the enhancement of both storage modulus and glass transition temperature of the former samples in comparison with reference pristine PBT. © 2017 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2018, 135, 46006.

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Shape memory behaviour of HA-g-PDLLA nanocomposites prepared via in situ polymerization

Cited by 41 publications

References 42 publications

Recent progress in shape memory polymer: New behavior, enabling materials, and mechanistic understanding

Recent progress in shape memory polymer: New behavior, enabling materials, and mechanistic understanding

Shape Memory Polymer–Inorganic Hybrid Nanocomposites

Thermo‐mechanical behavior of poly(butylene terephthalate)/silica nanocomposites

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