Preparation-free and skin compliant biopotential electrodes with high recording quality enable wearables for future healthcare and the Internet of Humans. Here, super-soft and self-adhesive electrodes are presented for use on dry and hairy skin without skin preparation or attachment pressure. The electrodes show a skin-contact impedance of 50 kΩ cm at 10 Hz that is comparable to clinical standard gel electrodes and lower than existing dry electrodes. Microstructured electrodes inspired by grasshopper feet adhere repeatedly to the skin with a force of up to 0.1 N cm without further attachment even during strong movement or deformation of the skin. Skin compliance and adhesive properties of the electrodes result in reduction of noise and motion artifacts superior to other dry electrodes reaching the performance of commercial gel electrodes. The signal quality is demonstrated by recording a high-fidelity electrocardiograms of a swimmer in water. Furthermore, an electrode with soft macropillars is used to detect alpha activity in the electroencephalograms from the back of the head through dense hair. Compared to gel electrodes, the soft biopotential electrodes are nearly imperceptible to the wearer and cause no skin irritations even after hours of application. The electrodes presented here could combine unobtrusive and long-term biopotential recordings with clinical-grade signal performance.

Filling of the electrode and the separator with an electrolyte is a crucial step in the lithium ion battery manufacturing process. Incomplete filling negatively impacts electrochemical performance, cycle life, and safety of cells. Here, we apply concepts from the theory of partial wetting to explain the amount of gas entrapment that occurs during electrolyte infilling and show that this can explain the lower than expected effective transport coefficients that are measured experimentally. We consider a polyethylene separator as a model system. Quasi-static infilling simulations on 3D reconstructions of the separator structure indicate that there can be up to 30% gas entrapment upon infilling due to the geometry of the separator, which results in a reduction of effective transport by >40%. Considering the dynamics of the electrolyte (e.g., viscosity) and the infilling process explains why the residual gas phase is typically less (15%–20%) and why, for electrolytes that wet well, increasing viscosity leads to higher values of gas entrapment, which is observed experimentally as decreased effective electrolyte conductivity. This work highlights the importance of optimizing not only the physiochemical properties of the electrolyte and pore surfaces, but also the 3D structure of the pore space, providing insights how to do so.

Accurate 3D representations of lithium-ion battery electrodes, in which the active particles, binder and pore phases are distinguished and labeled, can assist in understanding and ultimately improving battery performance. Here, we demonstrate a methodology for using deep-learning tools to achieve reliable segmentations of volumetric images of electrodes on which standard segmentation approaches fail due to insufficient contrast. We implement the 3D U-Net architecture for segmentation, and, to overcome the limitations of training data obtained experimentally through imaging, we show how synthetic learning data, consisting of realistic artificial electrode structures and their tomographic reconstructions, can be generated and used to enhance network performance. We apply our method to segment x-ray tomographic microscopy images of graphite-silicon composite electrodes and show it is accurate across standard metrics. We then apply it to obtain a statistically meaningful analysis of the microstructural evolution of the carbon-black and binder domain during battery operation.

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