A selective frequency damping and Janus adhesive hydrogel as bioelectronic interfaces for clinical trials.

Maintaining stillness is essential for accurate bioelectrical signal acquisition, but dynamic noise from breathing remains unavoidable. Isotropic adhesives are often used as bioelectronic interfaces to ensure signal fidelity, but they can leave irreversible residues, compromising device accuracy. We propose a hydrogel with selective frequency damping and asymmetric adhesion as a bioelectronic interface.

Topological MXene Network Enabled Mixed Ion-Electron Conductive Hydrogel Bioelectronics.

Mixed ion-electron conductive (MIEC) bioelectronics has emerged as a state-of-the-art type of bioelectronics for bioelectrical signal monitoring. However, existing MIEC bioelectronics is limited by delamination and transmission defects in bioelectrical signals. Herein, a topological MXene network enhanced MIEC hydrogel bioelectronics that simultaneously exhibits both electrical and mechanical property enhancement while maintaining adhesion and biocompatibility, providing an ideal MIEC bioelectronics for electrophysiological signal monitoring, is introduced.

Antimony Isotope Fractionation during Adsorption on Iron (Oxyhydr)oxides.

The fate of antimony (Sb) is strongly affected by adsorption, yet Sb isotope fractionation and the associated mechanism have not been widely reported. Here we experimentally investigated the process of Sb(V) adsorption on iron (oxyhydr)oxides and the associated isotope effects. Sb isotope fractionation occurs during adsorption (ΔSb = 1.

MXene-Enabled Self-Adaptive Hydrogel Interface for Active Electroencephalogram Interactions.

Human-machine interaction plays a significant role in promoting convenience, production efficiency, and usage experience. Because of the universality and characteristics of electroencephalogram (EEG) signals, active EEG interaction is a promising and cutting-edge method for human-machine interaction. The seamless, skin-compliant, and motion-robust human-machine interface (HMI) for active EEG interaction has been in focus.

Vision-based movement recognition reveals badminton player footwork using deep learning and binocular positioning.

Coordinating dynamic interceptive actions in sports like badminton requires skilled performance in getting the racket into the right place at the right time. For this reason, the strategic movement and placement of one's feet, or , is an important part of competitive performance. Developing an automated, efficient, and economical method to record individual movement characteristics of players is critical and can benefit athletes and motor control specialists.