Phase-separated porous nanocomposite with ultralow percolation threshold for wireless bioelectronics

Abstract

Realizing the full potential of stretchable bioelectronics in wearables, biomedical implants and soft robotics necessitates conductive elastic composites that are intrinsically soft, highly conductive and strain resilient. However, existing composites usually compromise electrical durability and performance due to disrupted conductive paths under strain and rely heavily on a high content of conductive filler. Here we present an in situ phase-separation method that facilitates microscale silver nanowire assembly and creates self-organized percolation networks on pore surfaces. The resultant nanocomposites are highly conductive, strain insensitive and fatigue tolerant, while minimizing filler usage. Their resilience is rooted in multiscale porous polymer matrices that dissipate stress and rigid conductive fillers adapting to strain-induced geometry changes. Notably, the presence of porous microstructures reduces the percolation threshold (V_c = 0.00062) by 48-fold and suppresses electrical degradation even under strains exceeding 600%. Theoretical calculations yield results that are quantitatively consistent with experimental findings. By pairing these nanocomposites with near-field communication technologies, we have demonstrated stretchable wireless power and data transmission solutions that are ideal for both skin-interfaced and implanted bioelectronics. The systems enable battery-free wireless powering and sensing of a range of sweat biomarkers-with less than 10% performance variation even at 50% strain. Ultimately, our strategy offers expansive material options for diverse applications.

Fig. 1. Schematic and fabrication of strain insensitive PSPN with ultralow percolation thresholds.

a, Schematic illustration of a cross-sectional view of the phase-separation process. b, c, Schematic of porous (b) and nonporous (c) Ag NWs nanocomposites at original (left) and stretched (right) states. d, Schematic illustration of the isothermal ternary phase diagram of polymer, solvent, and nonsolvent (i.e., PU, THF, and ethanol). The composition path in the phase diagram illustrates the trajectory of the multi-component system as it transitions across various phases due to the evaporation of the solvent and nonsolvent. The initial solution represents the composition of the original casting solution at the outset of this process. In brief, the initial casting solution, composed of solvent, nonsolvent, and polymer, forms a homogeneous solution. Upon the evaporation of solvent and nonsolvent, the trajectory of the composition path crosses the binodal and spinodal, entering the unstable region where liquid-liquid demixing occurs (two phases). The spinodal decomposition reduces the free energy of the system through diffusion and fluid flow or convection. Such mass transfer leads to the formation of bicontinuous porous structures. e, Numerical calculation of evolutions of electrical resistances as a function of applied strain on nonporous (black) and porous (red) nanocomposites. Dashed lines depict the exponentially fitted data. f, Theoretical calculation of percolation thresholds for nonporous (purple) and porous (red) Ag NWs nanocomposites, demonstrating its dependency on pore sizes. g, Optical photograph of a 500% stretched, free-standing PSPN. Scale bar, 1 cm.

Fig. 2. Phase separation, electrical and mechanical characterizations.

a, Scanning electron microscope (SEM) micrographs of PSPN with increased Ag NWs concentrations. Scale bars, 2 μm. b, Electrical conductivities of nonporous composite (orange) and PSPN (black, pore size, ~6.4 μm) as a function of Ag NWs volume fractions. Error bars represent standard deviations of the mean from six samples. Data are fitted using the 3D percolation theory. c, SEM images of PSPN before (left) and after stretching (middle, 50%; right, 100%). Scale bars, 5 μm. d, Relative resistance change (R/R₀) of nonporous (black) and porous (red) Ag NWs nanocomposites as a function of uniaxial strains. e, Comparison of electromechanical characteristics with other reported elastic conductors. Data were extracted from prior reports as summarized in Supplementary Fig. 10. f, Relative resistance changes of nonporous (orange) and porous (blue) nanocomposites subjected to cyclic stretching (50% maximum strain) for 10,000 cycles. Inset shows magnified resistance variations over a 20-cycle period. g, Demonstration of PSPN resilience to punctures from a scalpel knife, hammer impact, twisting, and bending. h, Water vapor transmission rate (WVTR, left) and Young’s modulus (right) of nonporous nanocomposites and PSPN, illustrating substantial increase in breathability and reduction in modulus with the presence of porous microstructures. i, Radar chart of the characteristics of this work compared with other soft conductive composites. Note that breathability and strain-insensitivity are qualitative values. Detailed comparison can be found in Supplementary Tables 1 and 2. In short, our PSPN exhibits a distinctive combination of multiple desired features, such as ultralow percolation threshold, outstanding strain-insensitive electrical conductance, high stability, and others, which represents a valuable addition to existing conductive composites for stretchable electronics. The nonporous composites in (d) and (f) were achieved by collapsing the porous microstructure of PSPN with THF vapor treatment (60 °C, overnight).

Fig. 3. Radiofrequency properties of the stretchable PSPN coils under tensile strain.

a, Two-port network model of the stretchable wireless power transfer system with the corresponding magnetic field distribution. b, Numeric simulations of magnetic field distributions of the porous and nonporous nanocomposites under 50% strain. c, Inductance L, resistance R, and quality factor Q of porous (red circle) and nonporous (naval solid) nanocomposites at 50 MHz as functions of uniaxial tensile strains. d-f, Scattering parameters S₁₁ (d), S₂₁ (e), and power transfer efficiency η (f) of the two-port wireless power transfer system comprising a primary transmitter coil and a stretchable receiver coil made from PSPN under strain. Inset in f, theoretical calculations of induced voltage on the stretchable receiver coil at 50 MHz under strain. g, The evolutions of power transfer efficiency at 50 MHz of the PSPN (red circle) and nonporous nanocomposites (naval solid) as a function of tensile strain. h, Power transfer efficiency between the primary transmitter and porous nanocomposite receiver coils at various vertical distances. Extracted data at 50 MHz appears in the inset.

Fig. 4. Stretchable wireless powering system for wearable and implantable bioelectronics.

a, b, Schematic of a simplified structure (a) and Maxwell simulations (b) of magnetic field generated by a rigid (top) and soft transmitter (bottom) placed on various body parts with diameters of 1.5, 3.5 and 5.5 cm. Dotted lines represent the transmitter coils used for the simulation. c, Coupling coefficient between the soft transmitter and implanted receiver coils with varied lateral (red) and vertical (naval) distances. d, Coupling coefficient between the soft PSPN (red) or rigid (naval) transmitter coil and the implanted receiver coil with varied phantom radii (h=1 cm). e, Transfer efficiency of the WPT system comprising the stretchable PSPN transmitter and implanted receiver, as a function of tensile strain. Extracted efficiency at 50 MHz appear in the inset, indicating the strain-insensitive performance for up to 50% strain. Here, the phantom radius is 5.5 cm, and the implantation depth is 1 cm. f, Photographs of a stretchable wireless powering system. A stretchable transmitter coil made of PSPN was used to power a red LED device implanted in fish, demonstrating robust and reliable operation when stretched at 50% strain. Scale bars, 2 cm. g, Quantitative brightness of the LED wirelessly powered by the transmitter made of the PSPN and conventional Ag NWs under strain. h, Schematic illustration of the in vivo demonstration for the implantable wireless power transfer system. An implantable optoelectronic device is subcutaneously implanted between the skin and muscle on the ventral side of the animal. The transmitter wirelessly delivers RF power to the implanted optoelectronic device with precise control over its operation, while allowing for unrestricted movement of the animal. i, Photograph of an implanted mouse freely behaving in an NFC-powered setup. Scale bar, 2 cm.

Fig. 5. Wireless stretchable bioelectronics for multiplexed biochemical sensing.

a, Schematic illustration of the stretchable, battery-free, and wireless LC circuit–based bioelectronics for biochemical sensing. RE, reference electrode; WE, working electrode. b, Equivalent circuit, and operation principle of the wireless sensing system. c, d, Reflection coefficients (c) and frequency shifts (d) for wireless Na⁺ (1–200 mM), NH₄⁺ (20–2000 µM), and H⁺ (10⁻⁷–10⁻² M) monitoring. e, f, Frequency shifts (e) and sensitivity (f) of the wireless Na⁺ sensing system made of PSPN (red circle) and conventional Ag NWs (black solid) under strain. g, Alterations in sensitivity observed in the Na⁺ sensing system after subjecting the PSPN device to 1,000 repetitive stretching cycles at strain levels of 25% and 50%. h, Schematic of wireless on-body stretchable bioelectronics for multiplexed biochemical sensing. i, j, Reflection coefficients for wireless on-body glucose (i) and alcohol (j) monitoring. Calibration plots appear in the inset. k, Selectivity tests of the multiplexed sensing system. Glucose (top) and alcohol (bottom) sensor responses to the addition of target molecules and interfering biomarkers, including glucose (150 μM), Na⁺ (20 mM), K⁺ (5 mM), ethanol (20 mM). l, Photograph of a volunteer wearing a multiplexed wireless biochemical sensing system during cycling exercise. m-o, Illustration (m) and dynamic wireless analysis of sweat glucose and ethanol levels (n, o) with and without intake of food and drink over a course of 8 hours. Sweat secretion was triggered by a 10-min session of constant-load stationary cycling. Raw data from 4 to 6 hours in (n) is depicted in (o). Error bars in (d), (e), (g), (i) and (j) represent standard deviations of the mean from three samples.