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Creators/Authors contains: "Brown, Anna"

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  1. Emulsion-templated polymerizations are an attractive route to prepare porous materials that possess broad tunability by controlling the features of the emulsion template. Emulsion templated polymer materials possessing shape memory behavior have also been reported, usually using (meth)acrylate monomers. However, achieving shape memory properties in emulsion templated materials with polymers that do not possess accessible thermal transitions, including polydimethylsiloxane (PDMS), remains challenging. Here, porous PDMS materials have been prepared with stearic acid within the continuous phase of the emulsion template. The inclusion of stearic acid imparts the material with a transition temperature of ∼70 °C, and the porous materials in this work obtained fixity >90% and recovery >95% over multiple shape memory cycles. These results demonstrate how low glass-transition temperature emulsion-templated polymer materials can easily be given shape memory properties. This work should be a starting point for studies of elastomeric emulsion-templated polymer materials in applications, including in soft robotics. 
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  2. Climate change is one of the greatest challenges facing humanity, and we, as machine learning (ML) experts, may wonder how we can help. Here we describe how ML can be a powerful tool in reducing greenhouse gas emissions and helping society adapt to a changing climate. From smart grids to disaster management, we identify high impact problems where existing gaps can be filled by ML, in collaboration with other fields. Our recommendations encompass exciting research questions as well as promising business opportunities. We call on the ML community to join the global effort against climate change. 
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