A Personalized Electronic Tattoo for Healthcare Realized by On-the-Spot Assembly of an Intrinsically Conductive and Durable Liquid-Metal Composite.

Conventional electronic (e-) skins are a class of thin-film electronics mainly fabricated in laboratories or factories, which is incapable of rapid and simple customization for personalized healthcare. Here a new class of e-tattoos is introduced that can be directly implemented on the skin by facile one-step coating with various designs at multi-scale depending on the purpose of the user without a substrate. An e-tattoo is realized by attaching Pt-decorated carbon nanotubes on gallium-based liquid-metal particles (CMP) to impose intrinsic electrical conductivity and mechanical durability. Tuning the CMP suspension to have low-zeta potential, excellent wettability, and high-vapor pressure enables conformal and intimate assembly of particles directly on the skin in 10 s. Low-cost, ease of preparation, on-skin compatibility, and multifunctionality of CMP make it highly suitable for e-tattoos. Demonstrations of electrical muscle stimulators, photothermal patches, motion artifact-free electrophysiological sensors, and electrochemical biosensors validate the simplicity, versatility, and reliability of the e-tattoo-based approach in biomedical engineering.

Microfluidic Screening-Assisted Machine Learning to Investigate Vertical Phase Separation of Small Molecule:Polymer Blend.

Solution-based thin-film processing is a widely utilized technique for the fabrication of various devices. In particular, the tunability of the ink composition and coating condition allows precise control of thin-film properties and device performance. Despite the advantage of having such tunability, the sheer number of possible combinations of experimental parameters render it infeasible to efficiently optimize device performance and analyze the correlation between experimental parameters and device performance. In this work, a microfluidic screening-embedded thin-film processing technique is developed, through which thin-films of varying ratios of small molecule semiconductor:polymer blend are simultaneously generated and screened in a time- and resource-efficient manner. Moreover, utilizing the thin-films of varying combinations of experimental parameters, machine learning models are trained to predict the transistor performance. Gaussian Process Regression (GPR) algorithms tuned by Bayesian optimization shows the best predictive accuracy amongst the trained models, which enables narrowing down of the combinations of experimental parameters and investigation of the degree of vertical phase separation under the predicted parameter space. The technique can serve as a guideline for elucidating the underlying complex parameter-property-performance correlations in solution-based thin-film processing, thereby accelerating the optimization of various thin-film devices in the future.