Bouligand-Inspired Nanostructures Offer Superior Impact Resistance

NIST Bouligand-inspired nanomaterials

Published by Quantum Server Networks – June 2025

What do the Shinkansen bullet train, Velcro, and the mantis shrimp have in common? All are inspired by nature’s designs—and now, thanks to pioneering research from the National Institute of Standards and Technology (NIST), a new class of bioinspired nanomaterials may soon join that list. These materials take cues from the mantis shrimp’s punch-resistant exoskeleton, unlocking new ways to build stronger, longer-lasting materials for extreme environments.

Cracking the Code of Nature’s Armor

The mantis shrimp’s appendage can strike with the force of a .22 caliber bullet, all without damaging itself. The secret lies in microscopic “Bouligand structures”—helicoidal arrangements of fibers layered like twisted plywood. NIST researchers Sujin Lee and Edwin Chan replicated these structures using cellulose nanocrystals (CNCs), natural polymers found in plant fibers, which self-assembled into nanoplates and were stacked to create thin-film versions of the Bouligand design.

High-frequency sound waves helped align the CNCs, while a high-intensity laser fired silica microprojectiles at the films at speeds up to 600 m/s. The ultrafast camera footage showed the impacts in real time, revealing how the synthetic materials absorbed and deflected the shockwaves.

Designing Resilience: Lessons from the Shrimp

Key findings from the NIST study demonstrated that the mechanical response of the Bouligand-inspired films could be tuned by modifying the thickness and density of the nanocrystal layers. Thinner films retained more permanent indentations, while thicker ones redirected shockwaves—offering different modes of energy dissipation depending on the application.

This approach allows materials to be designed like a boxer in the ring—able to absorb punches round after round without faltering. From space debris-resistant satellite casings to next-generation military armor and sports helmets, the implications are far-reaching.

Beyond Biology: A Platform for Impact-Resistant Materials

This research supports NIST’s mission to develop metrology solutions that enhance industrial innovation. The methods and measurements from this study offer a blueprint for other scientists developing impact-resistant materials based on natural structures.

According to Chan, “These findings suggest there are different ways to design materials to absorb impact, and we can use this knowledge to create more resilient and longer-lasting materials.”

The original paper, published in the Proceedings of the National Academy of Sciences, is titled “Controlling Impact Mitigation via Bouligand Nanostructures” and provides a comprehensive roadmap for future materials science based on nature’s evolutionary ingenuity.

Read the original article at NIST: https://www.nist.gov/news-events/news/2025/06/bioinspired-materials-can-take-punch

Sponsored by PWmat (Lonxun Quantum) – a leading developer of GPU-accelerated materials simulation software for cutting-edge quantum, energy, and semiconductor research. Learn more about our solutions at: https://www.pwmat.com/en

📘 Download our latest company brochure to explore our software features, capabilities, and success stories: PWmat PDF Brochure

📞 Phone: +86 400-618-6006
📧 Email: support@pwmat.com

#BioinspiredMaterials #Nanotechnology #BouligandStructures #ImpactResistance #MaterialsScience #MantisShrimp #CelluloseNanocrystals #NIST #AdvancedMaterials #QuantumServerNetworks

Comments

Post a Comment

Popular posts from this blog

Quantum Chemistry Meets AI: A New Era for Molecular Machine Learning

Image
In a powerful leap forward for computational chemistry and materials design, researchers from Carnegie Mellon University have developed a new kind of molecular machine learning model—one that goes beyond simplistic molecular graphs and integrates fundamental principles of quantum chemistry. Their work, published in Nature Machine Intelligence , could radically enhance our ability to predict chemical behavior, optimize catalysts, and discover new drugs, all while using less data and gaining more scientific insight. The Problem: Traditional Molecular Representations Fall Short Molecular machine learning (ML) models underpin key processes in drug discovery, materials science, and catalysis. However, most existing models use simplified representations such as SMILES strings, 2D graphs, or coordinate matrices. These techniques often lack the quantum-chemical information that governs how molecules truly behave—particularly electron interactions that define reactivity, stability, and...
Read more

MIT’s React-OT: The AI Model Revolutionizing Chemical Reaction Design

Image
MIT’s React-OT: The AI Model Revolutionizing Chemical Reaction Design In a major leap for materials science and computational chemistry, researchers at MIT have unveiled a new AI-driven model called React-OT , capable of predicting the “point of no return” in chemical reactions — also known as the transition state — with remarkable speed and accuracy. Published in Phys.org on April 23, 2025, this innovation could revolutionize how scientists design sustainable chemical processes and novel materials. Why it matters: Understanding and predicting transition states is vital for designing efficient reactions in pharmaceuticals, polymers, and fuels. Traditional methods based on quantum chemistry are computationally expensive — sometimes taking days for a single calculation. Enter React-OT — a machine-learning model trained on over 9,000 reactions that slashes prediction time to under a second. The model’s secret? It begins with a smart est...
Read more

OMol25: A Record-Breaking Dataset Set to Revolutionize AI in Computational Chemistry

Image
Published on: Quantum Server Networks | Date: May 2025 In an era where artificial intelligence is reshaping every scientific frontier, a groundbreaking resource named Open Molecules 2025 (OMol25) has just been launched. This record-breaking dataset is poised to radically transform the way researchers model and simulate molecular interactions, accelerating innovation in materials science, biochemistry, and clean energy. Berkeley Lab News Center reports that the dataset, jointly developed by Meta’s Fundamental AI Research (FAIR) lab and the U.S. Department of Energy’s Lawrence Berkeley National Laboratory, encompasses over 100 million high-fidelity 3D molecular snapshots . These were generated using Density Functional Theory (DFT), a quantum mechanical modeling method renowned for its precision but traditionally limited by massive computational demands. What Makes OMol25 So Important? DFT simulations have long been a gold standard for predicting how atoms behave, includ...
Read more