Revealing the Stiffness of Materials with Light: Brillouin Spectroscopy Advances

Brillouin Spectroscopy Microscope

A groundbreaking development in materials science and biology has emerged with the application of Brillouin light scattering spectroscopy (BLS). This innovative technique allows researchers to measure a material’s stiffness using light, enabling unprecedented insights into the elastic properties of materials down to the cellular level.

Published in Nature Photonics, a new report highlights how BLS is being adopted by scientists worldwide to understand and characterize materials without physical contact or damage. At its core, this method measures tiny vibrations called phonons in a material by detecting subtle color shifts in laser light. These shifts reveal critical data about elasticity and sound speed within the material, opening doors to applications across physics, engineering, and life sciences.

Transforming Materials Characterization

Traditional methods of assessing stiffness often involve invasive mechanical probing or complex imaging. BLS revolutionizes this process by offering a non-invasive and highly precise approach, comparable in resolution to optical microscopy. This capability is particularly vital for soft biological tissues, where invasive techniques may cause damage or alter the material’s natural state.

Researchers at the University of Nottingham’s Optics and Photonics Research Group are among those pioneering this field. They have successfully applied BLS to study the stiffness of cancerous and healthy cells, plant roots, and even sperm cells, shedding light on how elasticity influences biological processes.

Implications for Cancer and Life Sciences

Assistant Professor Fernando Perez-Cota emphasized the transformative potential of this technology in life sciences. The ability to detect changes in cell stiffness could lead to earlier diagnoses of diseases like cancer, as metastatic cells often exhibit lower elasticity compared to healthy cells.

“Understanding these relationships has tremendous implications for our understanding of cancer and other diseases,” says Perez-Cota. “This global consensus in the scientific community is a step forward in standardizing practices and translating BLS technology into real-world applications.”

Standardizing a Global Effort

As BLS gains traction across laboratories worldwide, experts stress the importance of developing standardized protocols to ensure consistency and reliability in measurements. This collective effort aims to accelerate the technology’s journey from academic research to clinical and industrial applications.

For more details, read the original article from the University of Nottingham: Brillouin light scattering spectroscopy sheds light on material stiffness.

References

Nature Photonics (2025): Brillouin light scattering spectroscopy for stiffness characterization. DOI and further reading available via University of Nottingham News.

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

#BrillouinSpectroscopy #MaterialsScience #Photonics #CancerResearch #QuantumServerNetworks #SoftMatter #NonInvasiveImaging #ScientificInnovation

Comments

Post a Comment

Popular posts from this blog

Water Simulations Under Scrutiny: Researchers Confirm Methodological Errors

Image
Accurate water simulations are vital for research in biomolecular structures, drug discovery, and materials science. But a recent study from Oak Ridge National Laboratory (ORNL) has revealed a potential pitfall in long-standing computational methods, raising concerns about the reliability of countless molecular dynamics studies. The Time Step Problem in Simulations At the heart of the issue is the “time step” used in simulations—the small intervals at which calculations are performed. Since the 1970s, scientists have used time steps of 2 femtoseconds (quadrillionths of a second) to simulate molecular motion efficiently. However, the ORNL team has shown that using this standard can introduce significant errors when modeling liquid water, potentially leading to inaccurate predictions of properties such as density, pressure, and hydration energies. “Using longer time steps is tempting because it reduces computational cost,” explained senior scientist Dilip Asthagiri. “But a...
Read more

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

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