Tooth Diversity Underpins Future Biomimetic Replications

Abstract

Although the evolution of tooth structure seems highly conserved, remarkable diversity exists among species due to different living environments and survival requirements. Along with the conservation, this diversity of evolution allows for the optimized structures and functions of teeth under various service conditions, providing valuable resources for the rational design of biomimetic materials. In this review, we survey the current knowledge about teeth from representative mammals and aquatic animals, including human teeth, herbivore and carnivore teeth, shark teeth, calcite teeth in sea urchins, magnetite teeth in chitons, and transparent teeth in dragonfish, to name a few. The highlight of tooth diversity in terms of compositions, structures, properties, and functions may stimulate further efforts in the synthesis of tooth-inspired materials with enhanced mechanical performance and broader property sets. The state-of-the-art syntheses of enamel mimetics and their properties are briefly covered. We envision that future development in this field will need to take the advantage of both conservation and diversity of teeth. Our own view on the opportunities and key challenges in this pathway is presented with a focus on the hierarchical and gradient structures, multifunctional design, and precise and scalable synthesis.

SonoNERFs: Neural Radiance Fields applied to Biological Echolocation Systems allow 3D Scene Reconstruction Through Perceptual Prediction

“…While it has been observed that bats can predict aspects of the scene by accumulating sensor data [60], to the best of our knowledge, no concrete model on how this prediction might operate has been proposed in previous works. Other technical systems have been proposed to produce 3D scene reconstruction and semantic interpretation, [61], [62], but these proposed techniques utilize a teaching modality like LIDARs or cameras to perform a form of modality translation. Our SonoNERF model relies solely on acoustic data without the need for an additional supervision modality.…”

“…Our SonoNERF model relies solely on acoustic data without the need for an additional supervision modality. Furthermore, reference [62] does not use an acoustic sensing modality, causing the title to be somewhat misleading. Our approach follows the approach called ‘self-supervised learning’ which has received much attention in the recent years [63], [64].…”