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  1. Abstract

    The past ten years have seen the rapid expansion of the field of biohybrid robotics. By combining engineered, synthetic components with living biological materials, new robotics solutions have been developed that harness the adaptability of living muscles, the sensitivity of living sensory cells, and even the computational abilities of living neurons. Biohybrid robotics has taken the popular and scientific media by storm with advances in the field, moving biohybrid robotics out of science fiction and into real science and engineering. So how did we get here, and where should the field of biohybrid robotics go next? In this perspective, we first provide the historical context of crucial subareas of biohybrid robotics by reviewing the past 10+ years of advances in microorganism-bots and sperm-bots, cyborgs, and tissue-based robots. We then present critical challenges facing the field and provide our perspectives on the vital future steps toward creating autonomous living machines.

     
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  2. Abstract

    We present a new computational framework of neuron growth based on the phase field method and develop an open-source software package called “NeuronGrowth_IGAcollocation”. Neurons consist of a cell body, dendrites, and axons. Axons and dendrites are long processes extending from the cell body and enabling information transfer to and from other neurons. There is high variation in neuron morphology based on their location and function, thus increasing the complexity in mathematical modeling of neuron growth. In this paper, we propose a novel phase field model with isogeometric collocation to simulate different stages of neuron growth by considering the effect of tubulin. The stages modeled include lamellipodia formation, initial neurite outgrowth, axon differentiation, and dendrite formation considering the effect of intracellular transport of tubulin on neurite outgrowth. Through comparison with experimental observations, we can demonstrate qualitatively and quantitatively similar reproduction of neuron morphologies at different stages of growth and allow extension towards the formation of neurite networks.

     
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  3. Free, publicly-accessible full text available December 1, 2024
  4. Meder, F. (Ed.)
    Free, publicly-accessible full text available August 1, 2024
  5. Meder, F. ; Hunt, A. ; Margheri, L. ; Mura, A. ; Mazzolai, B. (Ed.)
    Free, publicly-accessible full text available August 1, 2024
  6. Hydrogels are candidate building blocks in a wide range of biomaterial applications including soft and biohybrid robotics, microfluidics, and tissue engineering. Recent advances in embedded 3D printing have broadened the design space accessible with hydrogel additive manufacturing. Specifically, the Freeform Reversible Embedding of Suspended Hydrogels (FRESH) technique has enabled the fabrication of complex 3D structures using extremely soft hydrogels, e.g., alginate and collagen, by assembling hydrogels within a fugitive support bath. However, the low structural rigidity of FRESH printed hydrogels limits their applications, especially those that require operation in nonaqueous environments. In this study, we demonstrated long-fiber embedded hydrogel 3D printing using a multihead printing platform consisting of a custom-built fiber extruder and an open-source FRESH bioprinter with high embedding fidelity. Using this process, fibers were embedded in 3D printed hydrogel components to achieve significant structural reinforcement (e.g., tensile modulus improved from 56.78 ± 8.76 to 382.55 ± 25.29 kPa and tensile strength improved from 9.44 ± 2.28 to 45.05 ± 5.53 kPa). In addition, we demonstrated the versatility of this technique by using fibers of a wide range of sizes and material types and implementing different 2D and 3D embedding patterns, such as embedding a conical helix using electrochemically aligned collagen fiber via nonplanar printing. Moreover, the technique was implemented using low-cost material and is compatible with open-source software and hardware, which facilitates its adoption and modification for new research applications. 
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