A research paper by scientists at Duke University proposed a novel sharp-edge acoustofluidic platform designed for rapid and effective sample preparation, coupled with sensitive detection of specific sEV populations based on their surface markers.

The new research paper, published on July. 17 in the journal Cyborg and Bionic Systems, presented an acoustofluidic technology which enables highly flexible, specific, and efficient capture and detection of circulating extracellular vesicles (sEVs) from small sample volumes. Its portability, low cost, and ease of use make it an ideal tool for point-of-care detection of sEV surface markers, while its modular design allows for one-step, high-throughput capture and detection of diverse sEV populations

Temperature sensing plays a pivotal role in controlling and monitoring industrial, chemical, and biomedical systems. There are some complex internal space structures such as porous and zigzag in microscale or constrained environments. Traditional temperature measurement techniques such as thermocouples, infrared sensors, and fiber optics face larger challenges in such specific environments. This is not only due to their size but also because of limitations in mechanical flexibility, spatial adaptability, or contact requirements restrictions. To overcome these challenges, researchers have explored various nanoscale thermometers, including quantum dots, lanthanide-doped nanoparticles, and nitrogen-vacancy (NV) centers in diamond. These temperature measurement methods offer high spatial resolution and a broad temperature measurement range, but they typically require continuous light excitation and real-time fluorescence detection. It makes them difficult to implement effectively in complex porous structures or confined internal spaces.

Recently, the research team led by Prof. Hanyang Li at Harbin Engineering University has integrated functionalized liquid crystal (LC) microcavities with whispering-gallery-mode (WGM) laser technology to establish a novel real-time biosensing platform, enabling highly sensitive detection of ALT.

This work proposes a novel lower-limb motion capture system that, for the first time, combines a flexible pressure sensor array with a Transformer-based temporal regression model. The system enables accurate estimation of lower-limb joint positions using only insole-embedded sensors.