Programmed multimaterial assembly by synergized 3D printing and freeform laser induction.

In nature, structural and functional materials often form programmed three-dimensional (3D) assembly to perform daily functions, inspiring researchers to engineer multifunctional 3D structures. Despite much progress, a general method to fabricate and assemble a broad range of materials into functional 3D objects remains limited. Herein, to bridge the gap, we demonstrate a freeform multimaterial assembly process (FMAP) by integrating 3D printing (fused filament fabrication (FFF), direct ink writing (DIW)) with freeform laser induction (FLI). 3D printing performs the 3D structural material assembly, while FLI fabricates the functional materials in predesigned 3D space by synergistic, programmed control. This paper showcases the versatility of FMAP in spatially fabricating various types of functional materials (metals, semiconductors) within 3D structures for applications in crossbar circuits for LED display, a strain sensor for multifunctional springs and haptic manipulators, a UV sensor, a 3D electromagnet as a magnetic encoder, capacitive sensors for human machine interface, and an integrated microfluidic reactor with a built-in Joule heater for nanomaterial synthesis. This success underscores the potential of FMAP to redefine 3D printing and FLI for programmed multimaterial assembly.

A Photocured Bio-based Shape Memory Thermoplastics for Reversible Wet Adhesion.

Development of reversible wet or underwater adhesives remains a grand challenge. Because weakened intermolecular interactions by water molecules or/and low effective contact area cause poor interface to the wet surfaces, which significantly decreases adhesive strength. Herein, a new photocured, bio-based shape memory polymer (SMP) that shows both chemical and structural wet adhesion to various types of surfaces is developed. The SMP is polymerized from three monomers mainly from bio-sources to form linear polymer chains dangled with hydrophobic side chains. The hydrogen acceptor and donor groups in the chains form hydrogen bonding with the surfaces, which is protected by the hydrophobic chains in the interface. The SMP shows tunable phase transition temperature (Tg) of 17-38 °C. In a rubbery state above Tg, the adhesive forms conformable contact with the targeted surfaces. Below Tg, a transition to a glassy state locks the conformed shapes to largely increase the effective contact area. As a result, the adhesive exhibits long-term underwater adhesion of > 15 days with the best adhesion strength of ~ 0.9 MPa. Its applications in leak repair, underwater on-skin sensors were demonstrated. This new, general strategy would pave avenues to designing bio-based, long-lasting, and reversible adhesives from renewable feedstocks for widespread applications.

Accelerate Synthesis of Metal-Organic Frameworks by a Robotic Platform and Bayesian Optimization.

Synthesis of materials with desired structures, e.g., metal-organic frameworks (MOFs), involves optimization of highly complex chemical and reaction spaces due to multiple choices of chemical elements and reaction parameters/routes. Traditionally, realizing such an aim requires rapid screening of these nonlinear spaces by experimental conduction with human intuition, which is quite inefficient and may cause errors or bias. In this work, we report a platform that integrates a synthesis robot with the Bayesian optimization (BO) algorithm to accelerate the synthesis of MOFs. This robotic platform consists of a direct laser writing apparatus, precursor injecting and Joule-heating components. It can automate the MOFs synthesis upon fed reaction parameters that are recommended by the BO algorithm. Without any prior knowledge, this integrated platform continuously improves the crystallinity of ZIF-67, a demo MOF employed in this study, as the number of operation iterations increases. This work represents a methodology enabled by a data-driven synthesis robot, which achieves the goal of material synthesis with targeted structures, thus greatly shortening the reaction time and reducing energy consumption. It can be easily generalized to other material systems, thus paving a new route to the autonomous discovery of a variety of materials in a cost-effective way in the future.