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Generative Artificial Intelligence (AI) tools, including language models that can create novel content, have shown promise for science communication at scale. However, these tools may show inaccuracies and biases, particularly against marginalized populations. In this work, we examined the potential of GPT-4, a generative AI model, in creating content for climate science communication. We analyzed 100 messages generated by GPT-4 that included descriptors of intersecting identities, science communication mediums, locations, and climate justice issues. Our analyses revealed that community awareness and actions emerged as prominent themes in the generated messages, while systemic critiques of climate justice issues were not as present. We discuss how intersectional lenses can help researchers to examine the underlying assumptions of emerging technology before its integration into learning contexts. 

Water in the form of windborne fog droplets supports life in many coastal arid regions, where natural selection has driven nontrivial physical adaptation toward its separation and collection. For two species of Namib desert beetle whose body geometry makes for a poor filter, subtle modifications in shape and texture have been previously associated with improved performance by facilitating water drainage from its collecting surface. However, little is known about the relevance of these modifications to the flow physics that underlies droplets’ impaction in the first place. We find, through coupled experiments and simulations, that such alterations can produce large relative gains in water collection by encouraging droplets to “slip” toward targets at the millimetric scale, and by disrupting boundary and lubrication layer effects at the microscopic scale. Our results offer a lesson in biological fog collection and design principles for controlling particle separation beyond the specific case of fog-basking beetles.