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Molecules at a liquid/vapor interface have different organizations and mobilities from those in the bulk. These differences potentially influence the rate of crystal nucleation, but the effect remains imperfectly understood. We have measured the crystal nucleation rates at the surface and in the bulk of amorphous poscaconazole, a rod-like molecule known to have a preferred interfacial orientation. We find that surface nucleation is vastly enhanced over bulk nucleation, by ∼9 orders of magnitude, and selects a different polymorph (II) from bulk nucleation (I). This phenomenon mirrors the recently reported case of D-arabitol and stems from the similarity of anisotropic surface molecular packing to the structure of the surface-nucleating polymorph. In contrast to these two systems, the surface enhancement of nucleation is weaker (though still significant) in acetaminophen and in water and does not select a different polymorph. Together, the systems investigated to date all feature surface enhancement, not suppression, of crystal nucleation, and those showing a polymorphic change feature (1) structural reconstruction at the surface relative to the bulk and (2) existence of a different polymorph that can take advantage of the surface environment to nucleate. These results help predict the effect of a liquid/vapor interface on crystal nucleation and polymorph selection, especially in systems with a large surface/volume ratio, such as atmospheric water and amorphous particles.

 

Computing systems are omnipresent; their sustainability has become crucial for our society. A key aspect of this sustainability is the ability of computing systems to cope with the continuous change they face, ranging from dynamic operating conditions, to changing goals, and technological progress. While we are able to engineer smart computing systems that autonomously deal with various types of changes, handling unanticipated changes requires system evolution, which remains in essence a human-centered process. This will eventually become unmanageable. To break through the status quo, we put forward an arguable opinion for the vision of self-evolving computing systems that are equipped with an evolutionary engine enabling them to evolve autonomously. Specifically, when a self-evolving computing systems detects conditions outside its operational domain, such as an anomaly or a new goal, it activates an evolutionary engine that runs online experiments to determine how the system needs to evolve to deal with the changes, thereby evolving its architecture. During this process the engine can integrate new computing elements that are provided by computing warehouses. These computing elements provide specifications and procedures enabling their automatic integration. We motivate the need for self-evolving computing systems in light of the state of the art, outline a conceptual architecture of self-evolving computing systems, and illustrate the architecture for a future smart city mobility system that needs to evolve continuously with changing conditions. To conclude, we highlight key research challenges to realize the vision of self-evolving computing systems.