Converting Sound Waves into Electricity
For the first time, scientists from Lawrence Livermore National Laboratory (Livermore, California) have converted the highest frequency sound waves into electricity by reversing the process that converts electrical signals to sound.
For the first time, scientists from Lawrence Livermore National Laboratory (Livermore, California) have converted the highest frequency sound waves into electricity by reversing the process that converts electrical signals to sound. The researchers predicted that high-frequency acoustic waves could be detected by seeing radiation emitted when the acoustic wave passes an interface between piezoelectric materials.
Very high-frequency sound waves have wavelengths approaching the atomic-length scale. Although detection of these waves is challenging, they are useful for probing materials on very small length scales.
According to Evan Reed, one of the study’s lead researchers, that is not the only application. “This technique provides a new pathway to generation of terahertz (THz) radiation for security, medical, and other purposes,” he said. “In this application, we would utilize acoustic-based technologies to generate THz.” Medical applications of this technique might include detection of skin cancer, while security applications include explosives detection.
AI and Dual-Sensor Spectroscopy Supercharge Antibiotic Fermentation
June 30th 2025Researchers from Chinese universities have developed an AI-powered platform that combines near-infrared (NIR) and Raman spectroscopy for real-time monitoring and control of antibiotic production, boosting efficiency by over 30%.
Toward a Generalizable Model of Diffuse Reflectance in Particulate Systems
June 30th 2025This tutorial examines the modeling of diffuse reflectance (DR) in complex particulate samples, such as powders and granular solids. Traditional theoretical frameworks like empirical absorbance, Kubelka-Munk, radiative transfer theory (RTT), and the Hapke model are presented in standard and matrix notation where applicable. Their advantages and limitations are highlighted, particularly for heterogeneous particle size distributions and real-world variations in the optical properties of particulate samples. Hybrid and emerging computational strategies, including Monte Carlo methods, full-wave numerical solvers, and machine learning (ML) models, are evaluated for their potential to produce more generalizable prediction models.
