Understanding the Continuous Dynamic Mechanical Behavior of Animal Silk

Tendon Repair and Regeneration Using Bioinspired Fibrillation Engineering That Mimicked the Structure and Mechanics of Natural Tissue

Replicating the controlled nanofibrillar architecture of collagenous tissue represents a promising approach in the design of tendon replacements that have tissue-mimicking biomechanics─outstanding mechanical strength and toughness, defect tolerance, and fatigue and fracture resistance. Guided by this principle, a fibrous artificial tendon (FAT) was constructed in the present study using an engineering strategy inspired by the fibrillation of a naturally spun silk protein. This bioinspired FAT featured a highly ordered molecular and nanofibrillar architecture similar to that of soft collagenous tissue, which exhibited the mechanical and fracture characteristics of tendons. Such similarities provided the motivation to investigate FAT for applications in Achilles tendon defect repair. In vitro cellular morphology and expression of tendon-related genes in cell culture and in vivo modeling of tendon injury clearly revealed that the highly oriented nanofibrils in the FAT substantially promoted the expression of tendon-related genes combined with the Achilles tendon structure and function. These results provide confidence about the potential clinical applications of the FAT.

Mechanically and electrically biocompatible hydrogel ionotronic fibers for fabricating structurally stable implants and enabling noncontact physioelectrical modulation

Narrowing the mechanical and electrical mismatch between tissue and implantable microelectronics is essential for reducing immune responses and modulating physioelectrical signals. Nevertheless, the design of such implantable microelectronics remains a challenge due to the limited availability of suitable materials. Here, the fabrication of an electrically and mechanically biocompatible alginate hydrogel ionotronic fiber (AHIF) is reported, which is constructed by combing ionic chelation-assisted wet-spinning and mechanical training. The synergistic effects of these two processes allow the alginate to form a highly-oriented nanofibril and molecular network, with a hierarchical structure highly similar to that of natural fibers. These favourable structural features endow AHIF with tissue-mimicking mechanical characteristics, such as self-stiffening and soft tissue-like mechanical properties. In addition, tissue-like chemical components, i.e., biomacromolecules, Ca²⁺ ions, and water, endow AHIF with properties including biocompatibility and tissue-matching conductivity. These advantages bring light to the application of AHIFs in electrically-conductive implantable devices. As a prototype, an AHIF is designed to perform physioelectrical modulation through noncontact electromagnetic induction. Through experimental and machine learning optimizations, physioelectrical-like signals generated by the AHIF are used to identify the geometry and tension state of the implanted device in the body. Such an intelligent AHIF system has promising application prospects in bioelectronics, IntelliSense, and human-machine interactions.

Influence of Liquid Crystallinity and Mechanical Deformation on the Molecular Relaxations of an Auxetic Liquid Crystal Elastomer

Liquid Crystal Elastomers (LCEs) combine the anisotropic ordering of liquid crystals with the elastic properties of elastomers, providing unique physical properties, such as stimuli responsiveness and a recently discovered molecular auxetic response. Here, we determine how the molecular relaxation dynamics in an acrylate LCE are affected by its phase using broadband dielectric relaxation spectroscopy, calorimetry and rheology. Our LCE is an excellent model system since it exhibits a molecular auxetic response in its nematic state, and chemically identical nematic or isotropic samples can be prepared by cross-linking. We find that the glass transition temperatures (Tg) and dynamic fragilities are similar in both phases, and the T-dependence of the α relaxation shows a crossover at the same T* for both phases. However, for T>T*, the behavior becomes Arrhenius for the nematic LCE, but only more Arrhenius-like for the isotropic sample. We provide evidence that the latter behavior is related to the existence of pre-transitional nematic fluctuations in the isotropic LCE, which are locked in by polymerization. The role of applied strain on the relaxation dynamics and mechanical response of the LCE is investigated; this is particularly important since the molecular auxetic response is linked to a mechanical Fréedericksz transition that is not fully understood. We demonstrate that the complex Young's modulus and the α relaxation time remain relatively unchanged for small deformations, whereas for strains for which the auxetic response is achieved, significant increases are observed. We suggest that the observed molecular auxetic response is coupled to the strain-induced out-of-plane rotation of the mesogen units, in turn driven by the increasing constraints on polymer configurations, as reflected in increasing elastic moduli and α relaxation times; this is consistent with our recent results showing that the auxetic response coincides with the emergence of biaxial order.

Mechanical Training-Driven Structural Remodeling: A Rational Route for Outstanding Highly Hydrated Silk Materials

Highly hydrated silk materials (HHSMs) have been the focus of extensive research due to their usefulness in tissue engineering, regenerative medicine, and soft devices, among other fields. However, HHSMs have weak mechanical properties that limit their practical applications. Inspired by the mechanical training-driven structural remodeling strategy (MTDSRS) in biological tissues, herein, engineered MTDSRS is developed for self-reinforcement of HHSMs to improve their inherent mechanical properties and broaden potential utility. The MTDSRS consists of repetitive mechanical training and solvent-induced conformation transitions. Solvent-induced conformation transition enables the formation of β-sheet physical crosslinks among the proteins, while the repetitive mechanical loading allows the rearrangement of physically crosslinked proteins along the loading direction. Such synergistic effects produce strong and stiff mechanically trained-HHSMs (MT-HHSMs). The fracture strength and Young's modulus of the resultant MT-HHSMs (water content of 43 ± 4%) reach 4.7 ± 0.9 and 21.3 ± 2.1 MPa, respectively, which are 8-fold stronger and 13-fold stiffer than those of the as-prepared HHSMs, as well as superior to most previously reported HHSMs with comparable water content. In addition, the animal silk-like highly oriented molecular crosslinking network structure also provides MT-HHSMs with fascinating physical and functional features, such as stress-birefringence responsibility, humidity-induced actuation, and repeatable self-folding deformation.

Predicting the conformations of the silk protein through deep learning

As with other proteins, the conformation of the silk protein is critical for determining the mechanical, optical and biological performance of materials. However, an efficient, accurate and time-efficient method for evaluating the protein conformation from Fourier transform infrared (FTIR) spectra is still desired. A set of convolutional neural network (CNN)-based deep learning models was developed in this study to identify the silk proteins and evaluate their relative content of each conformation from FTIR spectra. Compared with the conventional deconvolution algorithm, our CNN models are highly accurate and time-efficient, showing promise in processing massive FTIR data sets, such as data from FTIR imaging, and in quick analysis feedback, such as on-line and time-resolved FTIR measurements. We compiled an open-source and user-friendly graphical Python program that allows users to analyze their own FTIR data set, which can be from the silk protein or other proteins, for the encouragement and convenience of interested researchers to use the CNN models.

Mechanism of Mechanical Training-Induced Self-Reinforced Viscoelastic Behavior of Highly Hydrated Silk Materials

Mechanical training is an operation where a sample is cyclically stretched in a solvent. It is accepted as an effective strategy to strengthen and stiffen the highly hydrated silk materials (HHSMs). However, the detailed reinforcement mechanism of the process still remains to be understood. Herein, this process is studied by the integration of experimental characterization and theoretical analysis. The results from time-resolved Fourier transform infrared spectroscopy and real-time birefringent characterization reveal that the silk proteins rapidly formed a molecular cross-linking network (MCN) during the mechanical training. The cross-links were the β-sheet nanocrystals generated from the conformation transition of silk proteins. With the progress in mechanical training, these MCNs gradually remodeled to a highly oriented molecular network structure. The final structure of the silk proteins in HHSMs is highly similar to the structural organization of silk proteins in the natural animal silk. The training process significantly improved the mechanical strength and modulus of the material. With regards to the dynamic behavior of conformation transition and MCN orientation, the structural evaluation of silk proteins during mechanical training was divided into three distinct stages, namely, the MCN-forming stage, MCN-orienting stage, and oriented-MCN stage. Such division is in complete agreement with the three-stage viscoelastic behavior observed in the cyclic loading and unloading tests. Hence, a five-parameter viscoelastic model has been established to elucidate the structure-property relationship of these three stages. This work improves in-depth understanding of the fundamental issues related to structure-property relationships of HHSMs and thus provides inspiration and guidance in the design of soft silk functional materials.