npj Flexible Electronics最新文献

Integrating surface-mounted devices (SMDs) onto textiles remains a key challenge in wearable electronics due to textile surface irregularities and heat sensitivity. Conventional methods like soldering or anisotropic conductive films (ACFs) often fail in such environments. We introduce a low-stress anisotropic conductive adhesive (LS-ACA) composed of eutectic gallium–indium (EGaIn) liquid metal particles (LMPs) embedded in a pressure-sensitive SIS matrix. LS-ACA offers excellent electrical conductivity, mechanical flexibility, and durability under bending, stretching, and crumpling. Finite element analysis shows it reduces interfacial stress concentrations compared to soldering, maintaining uniform stress even under 10% strain. It achieves ultra-low contact resistance (1.5 mΩ at >64 wt% LMPs) and enables low-temperature bonding on diverse substrates. Moreover, LS-ACA supports over 10 reuse cycles without surface damage or performance loss. This scalable, reusable material offers a promising path for integrating electronics into fabrics, advancing sustainable and flexible wearable technologies.

Tactile displays often face challenges like high power consumption, bulky control systems, and limited portability, hindering their application in wearable technologies. This work presents a novel thermo-pneumatic tactile display that operates via localized heating of a small air volume, enabling low-voltage operation with standard batteries. Its fully portable design integrates control electronics into a wearable bracelet with Bluetooth activation, enhancing practicality. Mechanical tests demonstrated the device’s ability to generate forces exceeding 30 mN and displacements of tens of microns using pulsed signals with modulable durations and frequencies. User tests with voluntary participants confirmed its effectiveness as a tactile display, achieving 83% accuracy in recognizing Braille patterns. By addressing key limitations of traditional systems, this approach offers a promising solution for compact, low-power wearable tactile interfaces.

Facial muscles are uniquely attached to the skin, densely innervated, and exhibit complex co-activation patterns enabling fine motor control. Facial surface Electromyography (sEMG) effectively assesses muscle function, yet traditional setups require precise electrode placement and limit mobility due to mechanical artifacts. Signal extraction is hindered by noise and cross-talk from adjacent muscles, making it challenging to associate facial muscle activity with expressions. We leverage a novel 16-channel conformal sEMG system to extract meaningful electrophysiological data from 32 healthy individuals. By applying denoising and source separation techniques, we extracted independent components, clustered them spatially, and built a facial muscle atlas. Furthermore, we established a functional mapping between these clusters and specific muscle units, providing a framework for understanding facial muscle activation. Using this foundation, we demonstrated a deep-learning model to predict facial expressions. This approach enables precise, participant-specific monitoring with applications in medical rehabilitation and psychological research.

Conductive polymers like poly(3,4-ethylenedioxythiophene):polystyrene sulfonate (PEDOT: PSS) are key materials in bioelectronics, but balancing ultrahigh conductivity with long-term tissue contact stability remains a challenge. Here, we present a solvent-mediated solid-liquid interface doping strategy to engineer vertically phase-separated (VPS) PEDOT: PSS films. By adjusting thickness and doping solvents, a thicker PEDOT: PSS film with a strong VPS structure was achieved, featuring a higher PSS/PEDOT ratio on the surface and a lower ratio at the bottom. Doping the pristine film with a metastable liquid-liquid contact solution enables gradual PSS migration and a significant component gradient, yielding films with a hydrophilic surface and one of the highest reported conductivities ( ~ 8800 S cm⁻¹) for bioelectronic devices. The films patterned by laser processing present high-fidelity signal acquisition, and excellent electrochemical stability. With low impedance and long-term biocompatibility, they are employed for real-time wearable and implantable sensors for electrophysiological monitoring, showcasing broad potentials in bioelectronics and human–machine interactions.

Neural representations arise from high-dimensional population activity, but current neuromodulation methods lack the precision to write information into the central nervous system at this complexity. In this perspective, we propose high-dimensional stimulation as an approach to better approximate natural neural codes for brain-machine interfaces. Key advancements in resolution, coverage, and safety are essential, with flexible microelectrode arrays offering a promising path toward precise synthetic neural codes.

Aiming at the poor selectivity of electrically conductive metal-organic framework (EC-MOF) chemoresistive materials, this study develops a breakthrough room temperature ammonia (NH₃) sensor by stacking ionically conductive MOF (IC-MOF) on an environmentally friendly biofabric. The synergism between ionic conductivity, tailored metal-nitrogen interaction, and fabric porosity enables the sensor with high response (R₀/R_g = 14.7 towards 1 ppm NH₃), low detection limit (36 ppb), and remarkable selectivity (coefficient >5.12 against common organic interferents). Notably, the optimized sensor yields a sixfold enhancement in response as compared with traditional EC-MOF powders. A linear regression model validated by fivefold cross-validation achieves 98.4% accuracy in NH₃ concentration prediction, while the kNN classifier shows 96% accuracy in gas identification (tested on 192 samples). Preliminary clinical tests show that the sensor can clearly differentiate the exhaled NH₃ signals of four patients with HE from those of healthy individuals, demonstrating the potential for non-invasive diagnostics.

This study presents dual-mode memory transistor that accommodates memory and synaptic operations utilizing photoinduced charge trapping at the interface between poly(1,4-butanediol diacrylate) (pBDDA) and Parylene dielectric layer. Memory characteristics were implemented based on the photoresponsivity of dinaphtho[2,3-b:2′,3′-f]thieno[3,2-b]thiophene (DNTT), enabling instantaneous electron storage under combined optical and electrical inputs, with retention times up to 10,000 s. Meanwhile, synaptic characteristics were induced by gradual charge trapping via optical pulse stimulation. Synaptic plasticity was confirmed via the potentiation–depression curve, emulating key features of biological nervous system, namely short-term memory (STM) and long-term memory (LTM). Furthermore, the fingerprint recognition tasks highlighted identification and authentication abilities by incorporating our synaptic function into an artificial neural network (ANN). The dual-mode memory transistor, fabricated on a business card, showed excellent compatibility with flexible optoelectronics, maintaining stable memory and synaptic performance over 500 bending cycles with minimal changes in memory window, memory ratio, and potentiation–depression behavior.

Iontronics presents a transformative paradigm for energy and information processing via ions as active charge carriers. Here, triboiontronics is introduced, a novel strategy leveraging contact electrification to achieve dynamic regulation of electrical double layers. Inspired by signaling mechanisms of biological neural systems, triboiontronics enables enhanced ionic-electronic coupling without external power input, offering a material-independent and self-powered pathway for programmable interfacial behavior, underscoring its promise for post-Moore, energy-autonomous information technologies.

Integrating stretchable and rigid electric units presents a significant challenge in manufacturing stretchable electronics. Their surface property differences prevented reliable stretching-tolerant connections. Here, we report a universal method to construct stretchable connections based on interfacial covalent reactions. It enables robust and conductive bonding among various soft/rigid electronics through simple surface modification and interfacial reaction. The bonding between SEBS rubber and metals reached stretchability over 250% with interfacial toughness over 200 N/m. The ultrathin connection layer provided conductive pathways, achieving an electrical stretchability of 60% between Au-deposited SEBS and Cu sheets. Connections between liquid metal-based stretchable conductors could withstand more than 10,000 stretching cycles to 60% strain while maintaining their high conductivity. The versatility and stability of this method were further proved by fabricating electronic devices that integrated soft and rigid units, including circuits on papers and a gesture-visualizing glove with LEDs, highlighting the robustness of the stretchable connections.

Intracortical microelectrodes are used for recording activity from individual neurons, providing both a valuable neuroscience tool and an enabling medical technology for individuals with motor disabilities. Standard neural probes carrying the microelectrodes are rigid silicon-based structures that can penetrate the brain parenchyma to interface with the targeted neurons. Unfortunately, within weeks after implantation, neural recording quality from microelectrodes degrades, owing largely to a neuroinflammatory response. Key contributors to the neuroinflammatory response include mechanical mismatch at the device-tissue interface and oxidative stress. We developed a mechanically-adaptive, resveratrol-eluting (MARE) neural probe to mitigate both mechanical mismatch and oxidative stress and thereby promote improved neural recording quality and longevity. In this work, we demonstrate that compared to rigid silicon controls, highly-flexible MARE probes exhibit improved recording performance, more stable impedance, and a healing tissue response. With further optimization, MARE probes can serve as long-term, robust neural probes for brain-machine interface applications.