Three-dimensional graphene coated shape memory polyurethane foam with fast responsive performance

Wang, Tianjiao; Zhao, Jun; Weng, Chuanxin; Wang, Tong; Liu, Yayun; Han, Zhipeng; Zhong, Ziyi

doi:10.1039/d1tc01315g

Cited by 29 publications

(25 citation statements)

References 46 publications

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“…One of the key candidates for this search has always been polymeric materials that exhibit shape memory properties. They come in a wide range of forms, from those that can be extracted from biological oils [72] to those that use carbon nanotube reinforcement [73], with many other forms of shape memory polymers and their composites currently under investigation [74,75]. Indeed, each class of such smart, stimuli-responsive shape-memory polymers is designed according to the specific requirements [76], e.g., programmed to a specific temporary shape and such that they can recover their original shape upon the application of various stimuli, depending on a specific application (temperature, electric and/or magnetic field, solvents, light irradiation, etc.).…”

Section: Data-driven Approaches For Studying Materials With Shape Mem...mentioning

confidence: 99%

Machine Learning for Shape Memory Graphene Nanoribbons and Applications in Biomedical Engineering

León

Melnik

2022

Bioengineering

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Shape memory materials have been playing an important role in a wide range of bioengineering applications. At the same time, recent developments of graphene-based nanostructures, such as nanoribbons, have demonstrated that, due to the unique properties of graphene, they can manifest superior electronic, thermal, mechanical, and optical characteristics ideally suited for their potential usage for the next generation of diagnostic devices, drug delivery systems, and other biomedical applications. One of the most intriguing parts of these new developments lies in the fact that certain types of such graphene nanoribbons can exhibit shape memory effects. In this paper, we apply machine learning tools to build an interatomic potential from DFT calculations for highly ordered graphene oxide nanoribbons, a material that had demonstrated shape memory effects with a recovery strain up to 14.5% for 2D layers. The graphene oxide layer can shrink to a metastable phase with lower constant lattice through the application of an electric field, and returns to the initial phase through an external mechanical force. The deformation leads to an electronic rearrangement and induces magnetization around the oxygen atoms. DFT calculations show no magnetization for sufficiently narrow nanoribbons, while the machine learning model can predict the suppression of the metastable phase for the same narrower nanoribbons. We can improve the prediction accuracy by analyzing only the evolution of the metastable phase, where no magnetization is found according to DFT calculations. The model developed here allows also us to study the evolution of the phases for wider nanoribbons, that would be computationally inaccessible through a pure DFT approach. Moreover, we extend our analysis to realistic systems that include vacancies and boron or nitrogen impurities at the oxygen atomic positions. Finally, we provide a brief overview of the current and potential applications of the materials exhibiting shape memory effects in bioengineering and biomedical fields, focusing on data-driven approaches with machine learning interatomic potentials.

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Section: Data-driven Approaches For Studying Materials With Shape Mem...mentioning

confidence: 99%

Machine Learning for Shape Memory Graphene Nanoribbons and Applications in Biomedical Engineering

León

Melnik

2022

Bioengineering

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“…Polymer sponges are a kind of continuous natural three-dimensional (3D) soft templates, which can effectively form continuous filler interconnected networks through simple dipping or coating [ 7 – 9 ]. Long-range continuous electrical [ 10 – 12 ] and thermal network [ 13 – 15 ] is an essential structural feature for the formation of multifunctional polymer composites with high performance.…”

Section: Introductionmentioning

confidence: 99%

Interconnected MXene/Graphene Network Constructed by Soft Template for Multi-Performance Improvement of Polymer Composites

et al. 2022

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The multi-functionalization of polymer composites refers to the ability to connect multiple properties through simple structural design and simultaneously achieve multi-performance optimization. The large-scale design and mass production to realize the reasonable structure design of multifunctional polymer composites are urgently remaining challenges. Herein, the multifunctional MXene/graphene/polymer composites with three-dimensional thermally and electrically conductive network structures are fabricated via the utilization of the microstructure of the soft template, and a facile dispersion dip-coating approach. As a result, the polymer composites have a multi-performance improvement. At the MXene and graphene content of 18.7 wt%, the superior through-plane thermal conductivity of polymer composite is 2.44 W m−1 K−1, which is 1118% higher than that of the polymer matrix. The electromagnetic interference (EMI) shielding effectiveness of the sample reaches 43.3 dB in the range of X-band. And the mechanical property of the sample has advanced 4 times compared with the polymer matrix. The excellent EMI shielding and thermal management performance, along with the effortless and easy-to-scalable producing techniques, imply promising perspectives of the polymer composites in the next-generation smart electronic devices.

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“…Smart actuators with fast and reversible changes towards external stimuli, such as temperature, [1][2][3] electricity, 4-6 magnetism, 7,8 solvents, 9,10 and light, 11,12 have attractive potential in artificial muscles, soft robots, programmable devices etc. Among them, light-driven actuators offer the advantages of remote activation, spatiotemporal control and local response, because they do not require complex coupled instruments and can be freely manipulated in a noncontact manner.…”

Section: Introductionmentioning

confidence: 99%

A high-efficiency actuator with great photoinduced force based on a bioinspired gradient design

Liu

et al. 2022

J. Mater. Chem. C

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Three-dimensional graphene coated shape memory polyurethane foam with fast responsive performance

Abstract: Shape memory polymers (SMPs) that change shapes as designed by external stimuli have become one of the most promising materials as actuators, sensors, and deployable devices. However, their practical applications...

Cited by 29 publications

References 46 publications

Machine Learning for Shape Memory Graphene Nanoribbons and Applications in Biomedical Engineering

Machine Learning for Shape Memory Graphene Nanoribbons and Applications in Biomedical Engineering

Interconnected MXene/Graphene Network Constructed by Soft Template for Multi-Performance Improvement of Polymer Composites

A high-efficiency actuator with great photoinduced force based on a bioinspired gradient design

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