Meniscus-Guided Micro-Printing of Prussian Blue for Smart Electrochromic Display

Using energy-saving electrochromic (EC) displays in smart devices for augmented reality makes cost-effective, easily producible, and efficiently operable devices for specific applications possible. Prussian blue (PB) is a metal-organic coordinated compound with unique EC properties that limit EC display applications due to the difficulty in PB micro-patterning. This work presents a novel micro-printing strategy for PB patterns using localized crystallization of FeFe(CN)(6) on a substrate confined by the acidic-ferric-ferricyanide ink meniscus, followed by thermal reduction at 120 degrees C, thereby forming PB. Uniform PB patterns can be obtained by manipulating printing parameters, such as the concentration of FeCl3 center dot K3Fe(CN)(6), printing speed, and pipette inner diameter. Using a 0.1 M KCl (pH 4) electrolyte, the printed PB pattern is consistently and reversibly converted to Prussian white (CV potential range: -0.2-0.5 V) with 200 CV cycles. The PB-based EC display with a navigation function integrated into a smart contact lens is able to display directions to a destination to a user by receiving GPS coordinates in real time. This facile method for forming PB micro-patterns could be used for advanced EC displays and various functional devices

High strength aluminum alloys design via explainable artificial intelligence

Here, we have approached to discover new aluminum (Al) alloys with the assistance of artificial intelligence (A.I.) for the enhanced mechanical property. A high prediction rate of 7xxx series Al alloy was achieved via the Bayesian hyperparameter optimization algorithm. With the guide of A.I.-based recommendation algorithm, new Al alloys were designed that had an excellent combination of strength and ductility with a yield strength (YS) of 712 MPa and elongation (EL) of 19%, exhibiting a homogeneous distribution of nanoscale precipitates hindering dislocation movement during deformation. Adding Mg and Cu was found to be the critical factor that decides the relative ratio of strength and EL. We also demonstrate an explainable A.I. (XAI) system that reveals the relationship between input and output parameters. Our A.I. assistant system can accelerate the search for high-strength Al alloys for both experts and non-experts in the field of Al alloy design. (c) 2022 Published by Elsevier B.V