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ABSTRACT Multifunctional fibers with high mechanical strength enable advanced applications of smart textiles, robotics, and biomedicine. Herein, we reported a one-step degumming method to fabricate strong, stiff, and humidity-responsive smart cellulosic fibers from abundant natural grass. The facile process involves partially removing lignin and hemicellulose functioning as glue in grass, which leads to the separation of vessels, parenchymal cells, and cellulosic fibers, where cellulosic fibers are manufactured at kilogram scale. The resulting fibers show dense and unidirectional fibril structure at both micro- and nano-scales, which demonstrate high tensile strength of ∼0.9 GPa and Young's modulus of 72 GPa, being 13- and 14-times higher than original grass. Inspired by stretchable plant tendrils, we developed a humidity-responsive actuator by engineering cellulosic fibers into the spring-like structures, presenting superior response rate and lifting capability. These strong and smart cellulosic fibers can be manufactured at large scale with low cost, representing promising a fiber material derived from renewable and sustainable biomass. 

Abstract One possible solution against the accumulation of petrochemical plastics in natural environments is to develop biodegradable plastic substitutes using natural components. However, discovering all-natural alternatives that meet specific properties, such as optical transparency, fire retardancy and mechanical resilience, which have made petrochemical plastics successful, remains challenging. Current approaches still rely on iterative optimization experiments. Here we show an integrated workflow that combines robotics and machine learning to accelerate the discovery of all-natural plastic substitutes with programmable optical, thermal and mechanical properties. First, an automated pipetting robot is commanded to prepare 286 nanocomposite films with various properties to train a support-vector machine classifier. Next, through 14 active learning loops with data augmentation, 135 all-natural nanocomposites are fabricated stagewise, establishing an artificial neural network prediction model. We demonstrate that the prediction model can conduct a two-way design task: (1) predicting the physicochemical properties of an all-natural nanocomposite from its composition and (2) automating the inverse design of biodegradable plastic substitutes that fulfils various user-specific requirements. By harnessing the model’s prediction capabilities, we prepare several all-natural substitutes, that could replace non-biodegradable counterparts as exhibiting analogous properties. Our methodology integrates robot-assisted experiments, machine intelligence and simulation tools to accelerate the discovery and design of eco-friendly plastic substitutes starting from building blocks taken from the generally-recognized-as-safe database. 

Abstract One of the major challenges in the development of micro-combustors is heat losses that result in flame quenching, and reduced combustion efficiency and performance. In this work, a novel thermal barrier coating (TBC) using hexagonal boron nitride (h-BN) nanosheets as building blocks was developed and applied to a Swiss roll micro-combustor for determining its heat losses with increased temperatures inside the combustor that contributes to improved performance. It was found that by using the h-BN TBC, the combustion temperature of the micro-combustor increased from 850 K to 970 K under the same thermal loading and operational conditions. This remarkable temperature increase using the BN TBC originated from its low cross-plane thermal conductivity of 0.4 W m−1 K−1to mitigate the heat loss from the micro-combustor plates. Such a low thermal conductivity in the h-BN TBC is attributed to its interfacial resistance between the nanosheets. The development of h-BN TBC provides an effective approach to improve thermal management for performance improvements of gas turbine engines, rocket engines, and all various kinds of micro-combustors.