Dataset Information

Artificial 3D hierarchical and isotropic porous polymeric materials.

ABSTRACT: Hierarchical porous materials that replicate complex living structures are attractive for a wide variety of applications, ranging from storage and catalysis to biological and artificial systems. However, the preparation of structures with a high level of complexity and long-range order at the mesoscale and microscale is challenging. We report a simple, nonextractive, and nonreactive method used to prepare three-dimensional porous materials that mimic biological systems such as marine skeletons and honeycombs. This method exploits the concurrent occurrence of the self-assembly of block copolymers in solution and macrophase separation by nucleation and growth. We obtained a long-range order of micrometer-sized compartments. These compartments are interconnected by ordered cylindrical nanochannels. The new approach is demonstrated using polystyrene-b-poly(t-butyl acrylate), which can be further explored for a broad range of applications, such as air purification filters for viruses and pollution particle removal or growth of bioinspired materials for bone regeneration.

SUBMITTER: Chisca S

PROVIDER: S-EPMC5947983 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature

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Artificial 3D hierarchical and isotropic porous polymeric materials.

Chisca Stefan S Musteata Valentina-Elena VE Sougrat Rachid R Behzad Ali Reza AR Nunes Suzana P SP

Science advances 20180511 5

Hierarchical porous materials that replicate complex living structures are attractive for a wide variety of applications, ranging from storage and catalysis to biological and artificial systems. However, the preparation of structures with a high level of complexity and long-range order at the mesoscale and microscale is challenging. We report a simple, nonextractive, and nonreactive method used to prepare three-dimensional porous materials that mimic biological systems such as marine skeletons a ...[more]

PMID: 29756039

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Project description:Given the importance of hyperelastic constitutive models in the design of engineering components, researchers have been developing the improved and new constitutive models in search of a more accurate and even universal performance. Here, a modified hyperelastic constitutive model based on the Yeoh model is proposed to improve its prediction performance for multiaxial deformation of hyperelastic polymeric materials while retaining the advantages of the original Yeoh model. The modified constitutive model has one more correction term than the original model. The specific form of the correction term is a composite function based on a power function represented by the principal stretches, which is derived from the corresponding residual strain energy when the Yeoh model predicts the equibiaxial mode of deformation. In addition, a parameter identification method based on the cyclic genetic-pattern search algorithm is introduced to accurately obtain the parameters of the constitutive model. By applying the modified model to the experimental datasets of various rubber or rubber-like materials (including natural unfilled or filled rubber, silicone rubber, extremely soft hydrogel and human brain cortex tissue), it is confirmed that the modified model not only possesses a significantly improved ability to predict multiaxial deformation, but also has a wider range of material applicability. Meanwhile, the advantages of the modified model over most existing models in the literatures are also demonstrated. For example, when characterizing human brain tissue, which is difficult for most existing models in the literature, the modified model has comparable predictive accuracy with the third-order Ogden model, while maintaining convexity in the corresponding deformation domain. Moreover, the effective prediction ability of the modified model for untested equi-biaxial deformation of different materials has also been confirmed using only the data of uniaxial tension and pure shear from various datasets. The effective prediction for the untested equibiaxial deformation makes it more suitable for the practice situation where the equibiaxial deformation of certain polymeric materials is unavailable. Finally, compared with other parameter identification methods, the introduced parameter identification method significantly improves the predicted accuracy of the constitutive models; meanwhile, the uniform convergence of introduced parameter identification method is also better.

Dataset Information

Artificial 3D hierarchical and isotropic porous polymeric materials.

Publications

Artificial 3D hierarchical and isotropic porous polymeric materials.

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