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Abstract Elastomers generally possess low Young's modulus and high failure strain, which are widely used in soft robots and intelligent actuators. However, elastomers generally lack diverse functionalities, such as stimulated shape morphing, and a general strategy to implement these functionalities into elastomers is still challenging. Here, a microfluidic 3D droplet printing platform is developed to design composite elastomers architected with arrays of functional droplets. Functional droplets with controlled size, composition, position, and pattern are designed and implemented in the composite elastomers, imparting functional performances to the systems. The composited elastomers are sensitive to stimuli, such as solvent, temperature, and light, and are able to demonstrate multishape (bow‐ and S‐shaped), multimode (gradual and sudden), and multistep (one‐ and two‐step) deformations. Based on the unique properties of droplet‐embedded composite elastomers, a variety of stimuli‐responsive systems are developed, including designable numbers, biomimetic flowers, and soft robots, and a series of functional performances are achieved, presenting a facile platform to impart diverse functionalities into composite elastomers by microfluidic 3D droplet printing. 

Abstract. Mineral dust is the most abundant aerosol species by massin the atmosphere, and it impacts global climate, biogeochemistry, and humanhealth. Understanding these varied impacts on the Earth system requiresaccurate knowledge of dust abundance, size, and optical properties, and howthey vary in space and time. However, current global models show substantialbiases against measurements of these dust properties. For instance, recentstudies suggest that atmospheric dust is substantially coarser and moreaspherical than accounted for in models, leading to persistent biases inmodelled impacts of dust on the Earth system. Here, we facilitate moreaccurate constraints on dust impacts by developing a new dataset: DustConstraints from joint Observational-Modelling-experiMental analysis(DustCOMM). This dataset combines an ensemble of global model simulationswith observational and experimental constraints on dust size distributionand shape to obtain more accurate constraints on three-dimensional (3-D)atmospheric dust properties than is possible from global model simulationsalone. Specifically, we present annual and seasonal climatologies of the 3-Ddust size distribution, 3-D dust mass extinction efficiency at 550 nm, andtwo-dimensional (2-D) atmospheric dust loading. Comparisons with independentmeasurements taken over several locations, heights, and seasons show thatDustCOMM estimates consistently outperform conventional global modelsimulations. In particular, DustCOMM achieves a substantial reduction in thebias relative to measured dust size distributions in the 0.5–20 µmdiameter range. Furthermore, DustCOMM reproduces measurements of dust massextinction efficiency to almost within the experimental uncertainties,whereas global models generally overestimate the mass extinction efficiency.DustCOMM thus provides more accurate constraints on 3-D dust properties, andas such can be used to improve global models or serve as an alternative toglobal model simulations in constraining dust impacts on the Earth system.