Various methods have been exploited to replicate nacre features into artificial structural materials with impressive structural and mechanical similarity. However, it is still very challenging to produce nacre-mimetics in three-dimensional bulk form, especially for further scale-up. Herein, we demonstrate that large-sized, three-dimensional bulk artificial nacre with comprehensive mimicry of the hierarchical structures and the toughening mechanisms of natural nacre can be facilely fabricated via a bottom-up assembly process based on laminating pre-fabricated two-dimensional nacre-mimetic films. By optimizing the hierarchical architecture from molecular level to macroscopic level, the mechanical performance of the artificial nacre is superior to that of natural nacre and many engineering materials. This bottom-up strategy has no size restriction or fundamental barrier for further scale-up, and can be easily extended to other material systems, opening an avenue for mass production of high-performance bulk nacre-mimetic structural materials in an efficient and cost-effective way for practical applications.

Bioinspired architectural design for composites with much higher fracture resistance than that of individual constituent remains a major challenge for engineers and scientists. Inspired by the survival war between the mantis shrimps and abalones, we design a discontinuous fibrous Bouligand (DFB) architecture, a combination of Bouligand and nacreous staggered structures. Systematic bending experiments for 3D-printed single-edge notched specimens with such architecture indicate that total energy dissipations are insensitive to initial crack orientations and show optimized values at critical pitch angles. Fracture mechanics analyses demonstrate that the hybrid toughening mechanisms of crack twisting and crack bridging mode arising from DFB architecture enable excellent fracture resistance with crack orientation insensitivity. The compromise in competition of energy dissipations between crack twisting and crack bridging is identified as the origin of maximum fracture energy at a critical pitch angle. We further illustrate that the optimized fracture energy can be achieved by tuning fracture energy of crack bridging, pitch angles, fiber lengths, and twist angles distribution in DFB composites.

Liquid crystal elastomer (LCE) is a newly emerging soft actuating material that has been extensively explored for building novel soft robots and diverse active devices, thanks to its large actuation stress and strain, high work density, and versatile actuation modes. However, there have also been several widely recognized limitations of LCE-based actuators for practical applications, including slow response and narrow range of operation temperature. Herein, we develop fluid-driven disulfide LCE actuators through facile laminate manufacturing enabled by a dynamic bond exchange reaction. Because of the merits of the active heating/cooling mechanism of the fluidic structure, this newly developed disulfide LCE actuator can generate large cyclic actuation at a frequency around 1 Hz and can operate in a wide range of temperatures. The unique combination of the fluidic structure design and the dynamic covalent bonds in the elastomer has also enabled the full recyclability and self-repairability of the actuator. Using the newly developed actuator as building block, we further constructed soft robotic systems that can realize manipulating and programmable movement. The design principle demonstrated in the current work opens a promising avenue for exploring more novel applications of LCE-based soft actuators.

Recently, liquid crystal elastomers (LCEs) have drawn much attention for its wide applications as artificial muscle in soft robotics, wearable devices, and biomedical engineering. One commonly adopted way to trigger deformation of LCEs is using embedded heating elements such as resistance heating wires and photothermal particles. To enable the material to recover to its unactuated state, passive and external cooling is often employed to lower the temperature, which is typically slow and environmentally sensitive. The slow and uncontrollable recovery speed of thermally driven artificial muscle often limits its applications when even moderate cyclic actuation rate is required. In this article, inspired by biology, a vascular LCE‐based artificial muscle (VLAM) is designed and fabricated with internal fluidic channel in which the hot or cool water is injected to heat up or cool down the material to achieve fast actuation as well as recovery. It is demonstrated that the actuation stress, strain, and cyclic response rate of the VLAM are comparable to mammalian skeletal muscle. Because of the internal heating and cooling mechanism, VLAM shows a very robust actuating performance within a wide range of environmental temperatures. The VLAM designed in this article may also provide a convenient way to harvest waste heat to conduct mechanical work.

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This paper presents a novel statistical framework for human cortical folding pattern analysis that relies on a rich multivariate descriptor of folding patterns in a region of interest (ROI). The ROIbased approach avoids problems faced by spatial-normalization-based approaches stemming from the deficiency of homologous features between typical human cerebral cortices. Unlike typical ROIbased methods that summarize folding by a single number, the proposed descriptor unifies multiple characteristics of surface geometry in a high-dimensional space (hundreds/thousands of dimensions). In this way, the proposed framework couples the reliability of ROI-based analysis with the richness of the novel cortical folding pattern descriptor. This paper presents new mathematical insights into the relationship of cortical complexity with intra-cranial volume (ICV). It shows that conventional complexity descriptors implicitly handle ICV Differences in Different ways, thereby lending Different meanings to "complexity". The paper proposes a new application of a non-parametric permutation-based approach for rigorous statistical hypothesis testing with multivariate cortical descriptors. The paper presents two cross-sectional studies applying the proposed framework to study folding Differences between genders and in neonates with complex congenital heart disease. Both studies lead to novel interesting results.

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Abstract. This paper presents a Bayesian framework for neonatal braintissue segmentation in clinical magnetic resonance (MR) images. This is a challenging task because of the low contrast-to-noise ratio and large variance in both tissue intensities and brain structures, as well as imaging artifacts and partial-volume effects in clinical neonatal scanning. We propose to incorporate a spatially adaptive likelihood model using a datadriven nonparametric statistical technique. The method initially learns an intensity-based prior, relying on the empirical Markov statistics from training data, using fuzzy nonlinear support vector machines (SVM). In an iterative scheme, the models adapt to spatial variations of image intensities via nonparametric density estimation. The method is effective even in the absence of anatomical atlas priors. The implementation, however, can naturally incorporate probabilistic atlas priors and Markovsmoothness priors to impose additional regularity on segmentation. The maximum-a-posteriori (MAP) segmentation is obtained within a graphcut framework. Cross validation on clinical neonatal brain-MR images demonstrates the efficacy of the proposed method, both qualitatively and quantitatively.