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.

Using Monte Carlo simulations combined with a geometric primitive path analysis method (Z1 algorithm), we investigate the effect of bidispersity on the structure and entanglement of polymer films which consist of short (the molecular length is below the characteristic entanglement molecular length) and long (the molecular length is above the characteristic entanglement molecular length) chains between two neutral walls. Our results demonstrate the length-based migrations of chains in bidisperse films (the longer chains reside away from the walls and the shorter chains are close to the walls), which becomes more obvious with the decrease in the weight fraction of long chains. With decreasing the weight fraction of long chains, the number of short-long entanglements exhibits a dramatic increase, whereas the number of long-long entanglements exhibits a slight decrease, which indicates that short chains can significantly affect the local situations of entanglements of bidisperse polymer films. On the basis of the constraint release mechanism, our simulations imply that for the lower weight fraction of long chains, the local degree of confinement instead of the long-long entanglements has a marked effect on the relaxation of long chains, due to the fast relaxation of short chains dilating the tube diameter of long chains. However, for the higher weight fraction of long chains, after the relaxation of short chains, the long-long entanglements are in sufficient quantities to restrict long chains within a tube, which implies that the relaxation of long chains is hardly affected by the number of short-long entanglements. Our work can be helpful for understanding the microscopic structure and entanglement of bidisperse polymer films, which can provide computational support for their various technological applications.

COVID‐19 (Corona Virus Disease 2019) has attacked many countries around world and caused profound impacts on public life. The outbreak of pandemic and other relevant factors are considered to cause emotion responses of residents. And the emotion responses of individuals are crucial for the execution of the prevention and control measures. By analyzing the linguistic features of posts on social media, this study aims to explore the change of public emotion responses during COVID‐19 in China. We sampled 22,423 Weibo users and collected their Weibo posts by provincial area each day from January 1st, 2020 to April 18th, 2020. Next, we extracted linguistic features from posts according to the emotion‐related dictionary. Based on important news and information released by the national and international organizations of public health, we divided the period from January 1st, 2020 to April 18th, 2020 into four stages (the initial period, the outbreak period, the stable period, and the prevention and control period). Then we gathered linguistic features by stage. After that, ANOVA was performed to examine the differences among these four stages. The results showed that the frequencies of 11 word categories showed significant differences among four stages, including fear, disappointment, guilt, missing, anger, panic, blessing, faith, love, praise, and surprise. The uses of several negative emotion words, such as fear, disappointment, guilt, and anger, increased saliently in the outbreak period compared with the initial period. Besides, panic words decreased significantly in the prevention and control period compared with the outbreak period. However, missing words were used more in the prevention and control period than other three periods. Moreover, people expressed more faith words and less love words in the outbreak period than the initial periods. Besides, people used more blessing words in the outbreak period compared with the stable period and prevention and control period. And praise words were used more in the outbreak period and the stable period compared with the initial period. The frequency of surprise words was significantly low only in the initial period. This study contributed to the understanding of public emotion responses during COVID‐19, and had implications for the evidence‐based execution of prevention and control measures.

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