The study aims to understand Twitter users' discourse and psychological reactions to COVID-19. We use machine learning techniques to analyze about 1.9 million Tweets (written in English) related to coronavirus collected from January 23 to March 7, 2020. A total of salient 11 topics are identified and then categorized into ten themes, including "updates about confirmed cases," "COVID-19 related death," "cases outside China (worldwide)," "COVID-19 outbreak in South Korea," "early signs of the outbreak in New York," "Diamond Princess cruise," "economic impact," "Preventive measures," "authorities," and "supply chain." Results do not reveal treatments and symptoms related messages as prevalent topics on Twitter. Sentiment analysis shows that fear for the unknown nature of the coronavirus is dominant in all topics. Implications and limitations of the study are also discussed.

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Noble-metal-free NiFeMo nanoparticles without any surfactant or support have been facilely synthesized and successfully applied as a highly efficient catalyst for the rapid and complete decomposition of hydrous hydrazine (a promising hydrogen storage/generation material) for hydrogen generation at a mild temperature. The surfactant/support-free nanoparticles possess good dispersion and small particle size. Moreover, upon the incorporation of Mo and Fe, the catalytic activity and hydrogen selectivity of the present trimetallic catalyst are remarkably improved compared with its mono-/bimetallic counterparts.