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. This hydrogel mitigates dynamic noise from breathing, with a damping effect in the breathing frequency range 60 times greater than at other frequencies. It also exhibits an asymmetric adhesion difference of up to 537 times, preventing residues. By homogenizing ion distribution, extending Debye length, and densifying the electric field, the hydrogel ensures stable signal transmission over 10,000 cycles. Additionally, it can non-invasively diagnose otitis media with higher sensitivity than invasive probes and has been effective in clinical polysomnography monitoring, aiding in the diagnosis of obstructive sleep apnea.

An afferent nerve-like electronic device with somatic mechanical perception and sensation management.

Tactile and pain perception are essential for biological skin to interact with the external environment. This complex interplay of sensations allows for the detection of potential threats and appropriate responses to stimuli. However, the challenge is to enable flexible electronics to respond to mechanical stimuli such as biological skin, and researchers have not clearly reported the successful integration of somatic mechanical perception and sensation management functions into neuro-like electronics. In this work, an afferent nerve-like device with a pressure sensor and a perception management module is proposed. The pressure sensor comprises two conductive fabric layers and an ionic hydrogel, forming a capacitor structure that emulates the swift transition from tactile to pain perception under mechanical stimulation. Drawing inspiration from the neuronal "gate control" mechanism, the sensation management module adjusts signals in response to rubbing, accelerating the discharge process and reducing the perception duration, thereby replicating the inhibitory effect of biological neurons on pain following tactile interference. This integrated device, encompassing somatic mechanical perception and sensation management, holds promise for applications in soft robotics, prosthetics, and human-machine interaction.

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. Compared with nontopology hydrogel bioelectronics, the MXene topology increases the dynamic stability of bioelectronics by a factor of 8.4 and the electrical signal by a factor of 10.1 and reduces the energy dissipation by a factor of 20.2. Besides, the topology-enhanced hydrogel bioelectronics exhibits low impedance (<25 Ω) at physiologically relevant frequencies and negligible impedance fluctuation after 5000 stretch cycles. The creation of multichannel bioelectronics with high-fidelity muscle action mapping and gait recognition was made possible by achieving such performance.

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 (Δ123Sbsolution-mineral = 1.20 ± 0.02‰ for ferrihydrite and 2.35 ± 0.04‰ for goethite). Extended X-ray absorption fine structure (EXAFS) analysis shows that Sb(V) adsorption on iron (oxyhydr)oxides occurs via inner-sphere surface complexation, including mononuclear bidentate edge-sharing (2E) and binuclear bidentate corner-sharing (2C) complexes. A longer atom distance of Sb-Fe in ferrihydrite leads to less Sb isotope fractionation during Sb adsorption than in goethite. The Gibbs free energy and Mayer bond order were calculated based on density functional theory (DFT) and suggested that the strength of the bonding environment can be summarized as Sb(OH)6- > 2E > 2C. In turn, the bonding environment indicates the mechanism of Sb isotope fractionation during the process. This study reveals that Sb isotope fractionation occurs during Sb(V) adsorption onto iron (oxyhydr)oxides, providing a basis for the future study of Sb isotopes and further understanding of the fractionation mechanism.

High-Performance Zwitterionic Organohydrogel Fiber in Bioelectronics for Monitoring Bioinformation.

Bioinformation plays an imperative role in day-to-day life. Wearable bioelectronics are important for sensing bioinformation in real-time and conductive hydrogel fibers are a key component in next generation wearable bioelectronics. However, current conductive hydrogel fibers have remarkable disadvantages such as insufficient conductivity, stability, and bioinformation sensing ability. Here, we report the synthesis of a zwitterionic organohydrogel (ZOH) fiber by the combination of the mold method and solvent replacement strategy. The ZOH fiber shows transparency (92.1%), stretchability (905.8%), long-term stability, anti-freezing ability (-35-60 °C), and low light transmission loss (0.17 dB/cm). Then, we integrate the ZOH fiber into fabric for use as a bioinformation sensor, the results prove its capability as a bioinformation monitor, monitoring information such as motion and bioelectric signals. In addition, the potential of the ZOH fiber in optogenetic applications is also confirmed.

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. Herein, we report a self-adaptive HMI (PAAS-MXene hydrogel) that can activate rapid gelation (5 s) using MXene cross-linking and conformably self-adapt to the scalp to help improve signal transduction. In addition to exhibiting satisfactory skin compliance, appropriate adhesion, and good biocompatibility, PAAS-MXene has demonstrated electrical performance reliability, such as low impedance (<50 Ω) at physiologically relevant frequencies, stable polarization potential (the rate of change is less than 6.5 × 10-4 V/min), negligible ion conductivity, and impedance change after 1000 stretch cycles, thereby realizing acquisition of EEG signals. In addition, a cap-free EEG signal acquisition method based on PAAS-MXene has been proposed. These findings confirm the high-precision detection ability of PAAS-MXene for electrocardiogram signals and EEG signals. Therefore, PAAS-MXene offers an option to actively control intention, motion, and vision through active EEG signals.

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 footwork, 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. Here, we propose new methods for recording data on the footwork of individual badminton players, in which deep learning is used to obtain image coordinates (2D) of their shoes and binocular positioning to reconstruct the 3D coordinates of the shoes. Results show that the final positioning accuracy is 74.7%. Using the proposed methods, we revealed inter-individual adaptations in the footwork of several participants during competitive performance. The data provided insights on how individual participants coordinated footwork to intercept the projectile, by varying the distance traveled on court and jump height. Compared with visual observations by biomechanists and motor control specialists, the proposed methods can obtain quantitative data, provide analysis and evaluation of each participant's performance, revealing personal characteristics that could be targeted to shape the individualized training programs of players to refine their badminton footwork.