Joint health is critical for musculoskeletal (MSK) conditions that are affecting approximately one-third of the global population. Monitoring of joint torque can offer an important pathway for the evaluation of joint health and guided intervention. However, there is no technology that can provide the precision, effectiveness, low-resource setting, and long-term wearability to simultaneously achieve both rapid and accurate joint torque measurement to enable risk assessment of joint injury and long-term monitoring of joint rehabilitation in wider environments. Herein, we propose a piezoelectric boron nitride nanotubes (BNNTs)-based, AI-enabled wearable device for regular monitoring of joint torque. We first adopted an iterative inverse design to fabricate the wearable materials with a Poisson’s ratio precisely matched to knee biomechanics. A highly sensitive piezoelectric film was constructed based on BNNTs and polydimethylsiloxane and applied to precisely capture the knee motion, while concurrently realizing self-sufficient energy harvesting. With the help of a lightweight on-device artificial neural network, the proposed wearable device was capable of accurately extracting targeted signals from the complex piezoelectric outputs and then effectively mapping these signals to their corresponding physical characteristics, including torque, angle, and loading. A real-time platform was constructed to demonstrate the capability of fine real-time torque estimation. This work offers a relatively low-cost wearable solution for effective, regular joint torque monitoring that can be made accessible to diverse populations in countries and regions with heterogeneous development levels, potentially producing wide-reaching global implications for joint health, MSK conditions, ageing, rehabilitation, personal health, and beyond.

Chihtong “Lily” Lee never set out to reinvent surgical tools, but her curiosity, precision and creativity led her to do just that. The 2025 Rice University graduate recently earned second place in the undergraduate category at the ASME SB3C Summer Bioengineering Conference, a competition hosted by the American Society of Mechanical Engineers.

A recent study investigates the contrasting patterns of symbiotic nitrogen fixation (SNF) and asymbiotic nitrogen fixation (ANF) along altitudinal gradients in subtropical forests. The research found that SNF rates declined with increasing altitude due to higher soil nitrogen availability and lower air temperatures, while ANF rates showed a hump-shaped pattern, influenced by soil properties at lower altitudes and climatic factors at higher altitudes. The study underscores the importance of distinguishing between SNF and ANF in ecological studies and Earth system models, providing valuable insights for improving global BNF estimates and refining model predictions.

The Vortex Particle Method (VPM) is a meshless vortex flow simulation approach gaining traction for its efficient simulation of unsteady vortex wakes evolution. However, traditional VPM has huge challenge on accurately simulating complex flows due to its poor numerical stability. Recently, a team of aviation researchers led by Min Chang from Northwestern Polytechnical University in China have developed a Stability-enhanced VPM (SEVPM). These advancements enable stable, high-fidelity simulations of complex flows. The researchers demonstrated that their SEVPM can accurately and stably simulate high Reynolds number flows and shear turbulence. The researchers plan to further validate and refine the Stability-enhanced VPM by applying it to more complex and realistic flow scenarios.