Jun Yao

Harnessing bio-origin resources for energy conversion offers a promising pathway toward energy sustainability matched to biosystems, addressing a fundamental limitation in powering bio-integrated electronics. However, existing approaches by integrating harvesters onto continuous substrates follow a “centralized” paradigm that differs markedly from the inherently distributed energy conversion found in living tissues, limiting both biocompatibility and scalability. We demonstrate a distributed harvesting strategy by integrating individual harvesting units within a mesh network that closely mimics the structural and mechanical properties of biological tissues. This architecture enables seamless embedding within in vitro cardiac tissue to form a bio-hybridization with cellular-scale intimacy across both device and substrate, transcending conventional approaches limited to a surface contact. This strategy achieves effective energy density more than an order of magnitude higher than that of existing approaches based on the centralized paradigm. While the concept is currently demonstrated in an in vitro cardiac system for converting biomechanical energy, the approach can provide a generalizable framework for integrating diverse energy-harvesting modalities and offers a pathway toward in vivo biohybrid systems.

Three-dimensional (3D) in vitro tissue models are emerging as powerful platforms for studying development, disease, and therapeutic responses, where close monitoring of electrophysiological activity is essential. However, existing probing methods remain limited in accessibility or spatiotemporal resolution for comprehensive electrophysiological mapping of suspended 3D tissues that closely mimic the native environments. Here we introduce a Venus flytrap-inspired bioelectronic mesh system that enables the full spherical enclosure of 3D tissues in a suspended configuration. The system consists of two hemispherical meshes that envelop the tissue, constructed from highly flexible, stretchable, cell-scale ribbons interconnected into a tissue-compliant network with integrated recording electrode arrays. This architecture enables intimate, conformal tissue integration and supports stable electrophysiological recordings over 300 days. The high-resolution recordings allow precise tracking of local dynamics and correlated global signaling, enabling comprehensive assessment of tissue development as well as detailed evaluation of drug responses for disease modeling. Beyond single tissues, the mesh architecture is extended to fully enclose assembloids composed of multiple tissues, enabling characterization of cross-tissue signaling relevant to advanced heterogenous tissue modeling. Furthermore, the system is translated into array-based platforms, demonstrated by a 4×4 array integrating 1024 electrodes, for high-throughput tissue sampling and cross-study analysis. The developed bioelectronic system and integration method provide a broadly applicable platform to advance electrophysiological studies across diverse tissues and organoids.

This paper introduces WISP, a printable graphene-based wearable system that enables truly imperceptible micro-gesture recognition with sub-10mm finger movements—up to 4 × smaller than existing approaches. While conventional gesture systems require conspicuous hand motions that disrupt social interactions, WISP detects subtle pinch-like micro-gestures that are virtually invisible to observers yet produce distinct force signatures at the wrist through tendon and skin deformation. Our co-designed hardware-software pipeline leverages Laser-Induced Graphene (LIG) technology to create ultra-thin, skin-conformal strain sensor arrays that can be fabricated on-demand using standard laser cutters and achieves 100μVpp noise floor while consuming only 10mW power. Evaluation with 19 participants demonstrates 99.2% hotword detection, 90.2% user-specific recognition, and 82.9% cross-user generalization. WISP maintains high accuracy across real-world conditions including walking and various clothing scenarios, scales to 12-gesture vocabularies, and extends to context-aware object interactions with 93.8% accuracy.

The efficient signal processing in biosystems is largely attributed to the powerful constituent unit of a neuron, which encodes and decodes spatiotemporal information using spiking action potentials of ultralow amplitude and energy. Constructing devices that can emulate neuronal functions is thus considered a promising step toward advancing neuromorphic electronics and enhancing signal flow in bioelectronic interfaces. However, existent artificial neurons often have functional parameters that are distinctly mismatched with their biological counterparts, including signal amplitude and energy levels that are typically an order of magnitude larger. Here, we demonstrate artificial neurons that not only closely emulate biological neurons in functions but also match their parameters in key aspects such as signal amplitude, spiking energy, temporal features, and frequency response. Moreover, these artificial neurons can be modulated by extracellular chemical species in a manner consistent with neuromodulation in biological neurons. We further show that an artificial neuron can connect to a biological cell to process cellular signals in real-time and interpret cell states. These results advance the potential for constructing bio-emulated electronics to improve bioelectronic interface and neuromorphic integration.

Free Electricity from Thin Air-Is It Really Possible Anywhere, Anytime?

Cardiac microtissues provide a promising platform for disease modeling and developmental studies, which require the close monitoring of the multimodal excitation-contraction dynamics. However, no existing assessing tool can track these multimodal dynamics across the live tissue. We develop a tissue-like mesh bioelectronic system to track these multimodal dynamics. The mesh system has tissue-level softness and cell-level dimensions to enable stable embedment in the tissue. It is integrated with an array of graphene sensors, which uniquely converges both bioelectrical and biomechanical sensing functionalities in one device. The system achieves stable tracking of the excitation-contraction dynamics across the tissue and throughout the developmental process, offering comprehensive assessments for tissue maturation, drug effects, and disease modeling. It holds the promise to provide more accurate quantification of the functional, developmental, and pathophysiological states in cardiac tissues, creating an instrumental tool for improving tissue engineering and studies.

Memristors are promising candidates for constructing neural networks. However, their dissimilar working mechanism to that of the addressing transistors can result in a scaling mismatch, which may hinder efficient integration. Here, we demonstrate two-terminal MoS₂ memristors that work with a charge-based mechanism similar to that in transistors, which enables the homogeneous integration with MoS₂ transistors to realize one-transistor-one-memristor addressable cells for assembling programmable networks. The homogenously integrated cells are implemented in a 2 × 2 network array to demonstrate the enabled addressability and programmability. The potential for assembling a scalable network is evaluated in a simulated neural network using obtained realistic device parameters, which achieves over 91% pattern recognition accuracy. This study also reveals a generic mechanism and strategy that can be applied to other semiconducting devices for the engineering and homogeneous integration of memristive systems.

Air humidity is a vast, sustainable reservoir of energy that, unlike solar and wind, is continuously available. However, previously described technologies for harvesting energy from air humidity are either not continuous or require unique material synthesis or processing, which has stymied scalability and broad deployment. Here, a generic effect for continuous energy harvesting from air humidity is reported, which can be applied to a broad range of inorganic, organic, and biological materials. The common feature of these materials is that they are engineered with appropriate nanopores to allow air water to pass through and undergo dynamic adsorption–desorption exchange at the porous interface, resulting in surface charging. The top exposed interface experiences this dynamic interaction more than the bottom sealed interface in a thin‐film device structure, yielding a spontaneous and sustained charging gradient for continuous electric output. Analyses of material properties and electric outputs lead to a “leaky capacitor” model that can describe how electricity is harvested and predict current behaviors consistent with experiments. Predictions from the model guide the fabrication of devices made from heterogeneous junctions of different materials to further expand the device category. The work opens a wide door for the broad exploration of sustainable electricity from air.

This review summarizes the development of memristors with functional voltages in the biological region (<120 mV), covering the aspects of device material composition, performance, working mechanism, and potential applications.