Shape memory elastomers: A review of synthesis, design, advanced manufacturing, and emerging applications

Prathumrat, Peerawat; Nikzad, Mostafa; Hajizadeh, Elnaz; Arablouei, Reza; Sbarski, Igor

doi:10.1002/pat.5652

Cited by 15 publications

(18 citation statements)

References 244 publications

(463 reference statements)

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“…117 In this regard, some wonderful reviews and perspectives have shown a comprehensive introduction to the topic, including the basic physical and chemical background of self-healing materials, 113,117−124 materials with different polymer structures, 118 emerging applications, 124 and the manufacturing of various self-healing materials. 124 The excellent biocompatibility and high transesterification efficiency make boronic acid/ester polymers have an outstanding research value and potential in reprocessable and healable materials. 116,125 However, the boronic acid/ester covalent network inevitably faces two flaws: (1) both boronate ester and boroxine are easily hydrolyzed and (2) the lower determined activation energy (E a ) of the boronate ester transesterification reaction affects the rigidity of materials.…”

Section: N-coordinated Boronic Acids or Esters In Healable And Reproc...mentioning

confidence: 99%

“…Numerous works on dynamic covalent chemistry, including thiol–disulfide exchange, olefin metathesis, cycloaddition, and transesterification reactions, have been reported . In this regard, some wonderful reviews and perspectives have shown a comprehensive introduction to the topic, including the basic physical and chemical background of self-healing materials, ,− materials with different polymer structures, emerging applications, and the manufacturing of various self-healing materials …”

Section: N-coordinated Boronic Acids or Esters In Healable And Reproc...mentioning

confidence: 99%

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N-Coordinated Organoboron in Polymer Synthesis and Material Science

Dong

et al. 2022

ACS Polym. Au

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Organoboron chemistry has been widely explored and developed in synthetic chemistry for over half a century and provides various elegant synthetic protocols in polymer synthesis. Compared with most trivalent bare organoboron compounds, Ncoordinated organoboron shows better performances, such as air and moisture stability. This review summarizes the application of various N-coordinated boranes and boronic acid/esters in polymer synthesis and materials science. We introduce the significance of N-coordinated boranes and boronate esters for controllable polymer synthesis and systematically summarize the structures and properties of polymers containing N-coordinated boronate esters. Furthermore, we highlight the effect of N→B dative bonds on improving the performance of self-healing materials. We hope that, through this review, more researchers will realize the advantages of N-coordinated organoboron and promote the development of this direction in polymer synthesis and materials science.

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Section: N-coordinated Boronic Acids or Esters In Healable And Reproc...mentioning

confidence: 99%

Section: N-coordinated Boronic Acids or Esters In Healable And Reproc...mentioning

confidence: 99%

N-Coordinated Organoboron in Polymer Synthesis and Material Science

Dong

et al. 2022

ACS Polym. Au

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“…Among them, heat is commonly used for activation [6,7]. The molecular architecture of the elastomer also plays a critical role in allowing the material to exhibit shape memory effects [8,9]. The SMEs typically consist of two prerequisite constituents, including netpoints and switch units.…”

Section: Introductionmentioning

confidence: 99%

“…Liquid crystalline elastomers (LCEs) are stimuli-responsive materials that have gained increasing attention for their potential shape memory effects [12,13]. The molecular structure of LCEs consists of mesogens (hard segment) and flexible tails (soft segment), allowing them to possess the typical underlying molecular mechanism of SMEs [8]. These materials can also respond to many external stimuli via the shape deformation process [12,[14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Lignin-liquid crystalline elastomeric composites for shape memory applications and their thermomechanical properties

Prathumrat¹,

Nikzad²,

Hajizadeh³

et al. 2023

Express Polym. Lett.

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Liquid crystalline elastomers (LCEs) are stimuli-responsive materials with potential use in shape memory applications. Though particularly suited for shape memory, the LCEs however have some drawbacks such as low shape fixity (R f ) and slow recovery time. To overcome these limitations, new lignin-filled elastomeric liquid crystalline (ELC) composite materials were fabricated. The lignin used is a by-product of Kraft pulping process, which was obtained from renewable resources abundant in nature. Here, we show that the aromatic structure of lignin increases the netpoints density at the microscopic level in the ELC composite systems. Shape memory effects are enhanced by incorporating up to only 7 wt% of lignin, resulting in an R f of 97% for the composites. Concurrently, these composites were able to maintain their shape recovery (R r ) of nearly 100%. The recovery time of the composites reduces with increasing lignin content due to the higher elastic energy released from the netpoints based on the lignin structure. The ELC composites with 7 wt% lignin could fully recover within 70 s, while the neat LCE counterpart took 100 s. Morphological features of dispersed lignin shows that even without surface modification and only a moderate quality of dispersion in LCE matrix, both shape memory and dynamic mechanical properties of the resultant composites can be significantly improved.

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“…To reduce the computational cost, machine learning and optimization techniques such as particle swarm optimization (PSO), genetic algorithm (GA), and simplex method 22,26,29 are integrated to enhance the efficiency of these methods. The emerging data-driven technique 31,43 is promising and is credible to achieve accuracy with enhanced efficiency compared to the conventional approaches 6 .…”

Section: Introductionmentioning

confidence: 99%

A novel machine learning enabled hybrid optimization framework for efficient and transferable coarse-graining of a model polymer

Shireen¹,

Weeratunge²,

Menzel³

et al. 2022

Preprint

Self Cite

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This work presents a novel framework governing the development of an efficient, accurate, and transferable coarsegrained (CG) model of a polyether material. The proposed framework combines the two fundamentally different classical optimization approaches for the development of coarse-grained model parameters; namely bottom-up and top-down approaches. This is achieved through integrating the optimization algorithms into a machine learning (ML) model, trained using molecular dynamics (MD) simulation data. In the bottom-up approach, bonded interactions of the CG model are optimized using deep neural networks (DNN), where atomistic bonded distributions are matched. The atomistic distributions emulate the local chain structure. In the top-down approach, optimization of nonbonded potentials is accomplished by reproducing the temperature-dependent experimental density. We demonstrate that CG model parameters achieved through our machine-learning enabled hybrid optimization framework fulfills the thermodynamic consistency and transferability issues associated with the classical approaches to coarse-graining model polymers. We demonstrate the efficiency, accuracy, and transferability of the developed CG model, using our novel framework through accurate predictions of chain size as well as chain dynamics, including the limiting behavior of the glass transition temperature, diffusion, and stress relaxation spectrum, where none were included in the potential parameterization process. The accuracy of the predicted properties are evaluated in the context of molecular theories and available experimental data.

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Shape memory elastomers: A review of synthesis, design, advanced manufacturing, and emerging applications

Cited by 15 publications

References 244 publications

N-Coordinated Organoboron in Polymer Synthesis and Material Science

N-Coordinated Organoboron in Polymer Synthesis and Material Science

Lignin-liquid crystalline elastomeric composites for shape memory applications and their thermomechanical properties

A novel machine learning enabled hybrid optimization framework for efficient and transferable coarse-graining of a model polymer

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