Stretchable Low-Impedance Conductor with Ag-Au-Pt Core-Shell-Shell Nanowires and in Situ Formed Pt Nanoparticles for Wearable and Implantable Device

Mechanically soft metallic nanocomposites have gained much attention as a key material for intrinsically stretchable biointegrated devices. However, it has been challenging to develop a stretchable conductive nanocomposite with all the desired material characteristics including high conductivity, high stretchability, low cytotoxicity, and low impedance. Here, we present a material strategy for the stretchable conductive nanocomposite, particularly emphasizing low impedance, by combining silver-gold-platinum core-shell-shell nanowires and homogeneously dispersed in situ synthesized platinum nanoparticles (Pt NPs). The highly embossed structure of the outermost Pt shell, together with the intrinsic electrical property of Pt, contributes to minimizing the impedance. The gold-platinum double-layer sheath prevents leaching of cytotoxic Ag ions, thus improving biocompatibility. Homogeneously dispersed Pt NPs, synthesized in situ during fabrication of the nanocomposite, simultaneously enhance conductivity, reduce impedance, and improve stretchability by supporting the percolation network formation. This intrinsically stretchable nanocomposite conductor can be applied to wearable and implantable bioelectronics for recording biosignals and delivering electrical stimulations in vivo.

Selective Ion Sweeping on Prussian Blue Analogue Nanoparticles and Activated Carbon for Electrochemical Kinetic Energy Harvesting

Kinetic energy is an ideal energy source for powering wearable devices or internet of things (IoTs) because of its abundant availability. Currently, most kinetic energy harvesting systems are based on friction or deformation, which require high-frequency motion or high material durability for sustainable energy harvesting. Here, we introduce selective ion sweeping in a hybrid cell consisting of an ion-adsorbing activated carbon and an ion-hosting Prussian blue analogue nanoparticle for electrochemical kinetic energy harvesting. The flow of electrolyte induced by kinetic motion of the cell causes ion sweeping only on the surface of the supercapacitor and induces current flow between the supercapacitor and the battery electrode. This method exhibits 24.9 μW cm^-2 as maximum power of system with 34 Ω load in half-cell test, which is several thousand times smaller than the load used in conventional methods. In a long-term test with full cell, this method supplies a continuous current flow ∼6 μA cm^-2 at the flow of 40 mL min^-1 for 500 cycles without performance decay. The prototype of the hybrid cell demonstrated kinetic energy harvesting from bare hand press with the various flow speeds from 0.41 to 1.39 cm s^-1 as well as walking, running, and door closing, which are representative examples of low-frequency kinetic motions in daily life. We believe that the simple structure of the hybrid cell will enable power supply to various applications from miniaturized systems (e.g., IoTs and wearables) to large-scale systems (e.g., ocean wave energy harvesting).

Quantification of Signal-to-Noise Ratio in Cerebral Cortex Recordings Using Flexible MEAs With Co-localized Platinum Black, Carbon Nanotubes, and Gold Electrodes

Developing new standardized tools to characterize brain recording devices is critical to evaluate neural probes and for translation to clinical use. The signal-to-noise ratio (SNR) measurement is the gold standard for quantifying the performance of brain recording devices. Given the drawbacks with the SNR measure, our first objective was to devise a new method to calculate the SNR of neural signals to distinguish signal from noise. Our second objective was to apply this new SNR method to evaluate electrodes of three different materials (platinum black, Pt; carbon nanotubes, CNTs; and gold, Au) co-localized in tritrodes to record from the same cortical area using specifically designed multielectrode arrays. Hence, we devised an approach to calculate SNR at different frequencies based on the features of cortical slow oscillations (SO). Since SO consist in the alternation of silent periods (Down states) and active periods (Up states) of neuronal activity, we used these as noise and signal, respectively. The spectral SNR was computed as the power spectral density (PSD) of Up states (signal) divided by the PSD of Down states (noise). We found that Pt and CNTs electrodes have better recording performance than Au electrodes for the explored frequency range (5–1500 Hz). Together with two proposed SNR estimators for the lower and upper frequency limits, these results substantiate our SNR calculation at different frequency bands. Our results provide a new validated SNR measure that provides rich information of the performance of recording devices at different brain activity frequency bands (<1500 Hz).