We use the optogalvanic method to calculate the concentration of rubidium ions produced by photoionization in a Rb diode-pumped alkali laser gain medium. With bias voltage added across the electrodes of a rubidium hollow cathode lamp, the measured optogalvanic current is 2.3×10(-7) A. Further study shows that the rubidium ion concentration is proportional to the pump intensity, and the drift velocity of rubidium ions is proportional to the bias voltage. When the photoionization process reaches dynamic equilibrium, the rubidium ion concentration will not increase with growing rubidium atom density. The calculated rubidium ion concentration is 1.5×10(5)-10(6) according to the experiment, and the ionization degree is less than 2.4×10(-7).
The declaration of COVID-19 as a pandemic has largely amplified the spread of related information on social medium, such as Twitter, Facebook and WeChat. Unlike the previous studies which focused on how to detect the misinformation or fake news related to COVID-19, we investigate how the disease and information co-evolve in the population. We focus on COVID-19 and its information during the period when the disease was widely spread in China, i.e., from January 25th to March 24th, 2020. We first explore how the disease and information co-evolve via the spatial analysis of the two spreading processes. We visualize the geo-location of both disease and information at the province level and find that disease is more geo-localized compared to information. We find high correlation between the disease and information data, and also people care about the spread only when it comes to their neighborhood. Regard to the content of the information, we find that positive messages are more negatively correlated with the disease compared to negative and neutral messages. Additionally, we introduce machine learning algorithms, i.e., linear regression and random forest, to further predict the number of infected using different characteristics, such as disease spatial related and informationrelated characteristics. We obtain that the disease spatial related characteristics of nearby cities can help to improve the prediction accuracy. Meanwhile, information-related characteristics can also help to improve the prediction performance, but with a delay, i.e., the improvement comes from using, for instance, the number of messages 10 days ago, for disease prediction. The methodology proposed in this paper may shed light on new clues of emerging infections prediction.
To improve the curing reaction rate and efficiency of sulfur-cured diene-based rubbers, the introduction of some chemical compounds as activators and accelerants is inevitably required, causing potential harm to humans and ecological systems. Moreover, silica is usually employed as a green filling material for rubber reinforcement, and a silane coupling agent is always required to improve its dispersion. Herein, we reported an effective method to cure hydroxyl-functionalized rubbers/silica composites with blocked polyisocyanates, avoiding the use of any other additives. The enhanced dispersion of silica by interaction with hydroxyl groups on molecular chains endowed the composites with high-mechanical performance. The mechanical properties and crosslinking kinetics of the resultant silica composites can be regulated by adjusting the content of hydroxyl groups in the rubber, as well as the amount of the blocked polyisocyanates. The dynamic heat build-up was related to the distance between crosslinking points. A SBROH/B-TDI/silica composite prepared with blocked toluene diisocyanatem (TDI) exhibited comparable tanδ (0.21 at 0 °C and 0.11 at 60 °C) to that of silica composites cured by sulfur with the help of a silane coupling agent (SBR/S/Si69/silica, 0.18 and 0.10), suggesting great applicable potential for new tire rubber compounds.
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