research

The International Journal of Solids and Structures is pleased to institute an annual Best Paper Award for Young Investigators. The award will be given annually to a paper published in the previous year whose principal or corresponding author is under the age of 38 or within 10 years of the completion of their PhD.

Hard-magnetic soft materials (HMSMs) consisting of an elastomer matrix filled with high remnant magnetic particles can exhibit flexible programmability and rapid shape changing under non-contact activation, showing promising potential applications in soft robotics, biomedical devices and flexible electronics. Precise predictions of large deformations of hard-magnetic soft materials would be a key for relevant applications.

One the main challenges of developing data-driven models is the data-hungry nature of Artificial Neural Networks (ANNs). In our recent paper, we introduced a data augmentation approach to expand a small original dataset without conducting extra expensive high-fidelity simulations. We then used the original and augmented datasets for developing ANN models for non-linear path dependent composites. The obtained results showed the great impact of the data augmentation approach on the accuracy of the data-driven models.

I am happy to share our paper entitled "Mechanisms of failure of aluminium-based Whipple shields under hypervelocity impact: insights from continuum simulations" published recently in Springer Nature journal Acta Mechanica.

Jie Ma, Xizhe Zhang, Daochen Yin, Yijie Cai, Zihang Shen, Zhi Sheng, Jiabao Bai, Shaoxing Qu, Shuze Zhu*, Zheng Jia*, Designing ultratough single-network hydrogels with centimeter-scale fractocohesive lengths via inelastic crack blunting, Advanced Materials, 2311795, 2024

Petals and leaves are usually curled and exhibit intriguing morphology evolution upon growth, which contributes to their important biological functions. To understand the underlying morphoelastic mechanism and to determine the crucial factors that govern the growth-induced instability patterning in curved petals and leaves, we develop an active thin shell model that can describe variable curvatures and spontaneous growth, within the framework of general differential geometry based on curvilinear coordinates and hyperelastic deformation theory.

The physical properties of graphene nanoribbons (GNRs) are closely related to their morphology; meanwhile GNRs can easily slide on surfaces (e.g., superlubricity), which may largely affect the configuration and hence the properties. However, the morphological evolution of GNRs during sliding remain elusive. We explore the intriguing tail swing behavior of GNRs under various sliding configurations on Au substrate. Two distinct modes of tail swing emerge, characterized by regular and irregular swings, depending on the GNR width and initial position relative to the substrate.