Equine Hoof Wall Deformation: Novel Aspects Revealed

Lazarus, Benjamin S.; Luu, Rachel K.; Ruiz-Pérez, Samuel; Barbosa, Josiane Dantas Viana; Jasiuk, Iwona; Meyers, Marc A.

doi:10.1002/sstr.202200402

Cited by 7 publications

(3 citation statements)

References 31 publications

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“…5b and 5c) reveals that the tubules are arranged parallel to the longitudinal direction and randomly distributed in the radial direction. Figure 5d shows a waviness in the tubular structure, which was previously reported by Lazarus et al [43]. This waviness might be caused by the dryness and loss of moisture from the hoof sample during the micro-CT scanning, which can result from the heat generated from the X-ray beam.…”

Section: Low-resolution µ-Ctsupporting

confidence: 70%

“…In the case of the hoof wall, collapsing pores could limit the maximum force transmitted to the internal structure of the hoof. Nanoscale pores observed from SBF-SEM would collapse and densify at very low strains, while the mesoscale tubules would collapse at higher strains [43]. Therefore, hierarchical porosity could be an essential structural feature for limiting the load transferred at different stages of impact.…”

Section: Sbf-semmentioning

confidence: 99%

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Multimodule imaging of the hierarchical equine hoof wall porosity and structure

Jasiuk

Mahrous

Chadha

et al. 2023

Preprint

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The equine hoof wall has a complex, hierarchical structure that can inspire designs of impact-resistant materials. In this study, we utilized micro-computed tomography (Micro CT) and serial block-face scanning electron microscopy (SBF-SEM) to image the microstructure and nanostructure of the hoof wall. We quantified the morphology of tubular medullary cavities by measuring equivalent diameter, surface area, volume, and sphericity. High-resolution Micro CT revealed that tubules are partially or fully filled with tissue near the exterior surface and become progressively empty towards the inner part of the hoof wall. Thin bridges were detected within the medullary cavity, starting in the middle section of the hoof wall and increasing in density and thickness towards the inner part. Porosity was measured using three-dimensional (3D) Micro CT, two-dimensional (2D) Micro CT, and a helium pycnometer, with the highest porosity obtained using the helium pycnometer (8.07%), followed by 3D (3.47%) and 2D (2.98%) Micro CT. SBF-SEM captured the 3D structure of the hoof wall at the nanoscale, showing that the tubule wall is not solid, but has nano-sized pores, which explains the higher porosity obtained using the helium pycnometer. The results of this investigation provide morphological information on the hoof wall for the future development of hoof-inspired materials and offer a novel perspective on how various measurement methods can influence the quantification of porosity.

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Section: Low-resolution µ-Ctsupporting

confidence: 70%

Section: Sbf-semmentioning

confidence: 99%

Multimodule imaging of the hierarchical equine hoof wall porosity and structure

Jasiuk

Mahrous

Chadha

et al. 2023

Preprint

Self Cite

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“…On the other hand, BioinspiredLLM provides a succinct response reporting the connection between hydrated hoof being more and drier hoof being more prone to brittle fracture. [45][46][47] Since BioinspiredLLM is trained specifically on mechanics of biological materials, the model does not require as much context to provide clear and concise answers.…”

Section: Which Of the Following Materials Has A Negative Poisson's Ratiomentioning

confidence: 99%

BioinspiredLLM: Conversational Large Language Model for the Mechanics of Biological and Bio‐Inspired Materials

Luu,

Buehler

2023

Advanced Science

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The study of biological materials and bio‐inspired materials science is well established; however, surprisingly little knowledge is systematically translated to engineering solutions. To accelerate discovery and guide insights, an open‐source autoregressive transformer large language model (LLM), BioinspiredLLM, is reported. The model is finetuned with a corpus of over a thousand peer‐reviewed articles in the field of structural biological and bio‐inspired materials and can be prompted to recall information, assist with research tasks, and function as an engine for creativity. The model has proven that it is able to accurately recall information about biological materials and is further strengthened with enhanced reasoning ability, as well as with Retrieval‐Augmented Generation (RAG) to incorporate new data during generation that can also help to traceback sources, update the knowledge base, and connect knowledge domains. BioinspiredLLM also has shown to develop sound hypotheses regarding biological materials design and remarkably so for materials that have never been explicitly studied before. Lastly, the model shows impressive promise in collaborating with other generative artificial intelligence models in a workflow that can reshape the traditional materials design process. This collaborative generative artificial intelligence method can stimulate and enhance bio‐inspired materials design workflows. Biological materials are at a critical intersection of multiple scientific fields and models like BioinspiredLLM help to connect knowledge domains.

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