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Students in rural China are dropping out of secondary school at troubling rates. While there is considerable quantitative research on this issue, no systematic effort has been made to assess the deeper reasons behind student decision making through a mixed-methods approach. This article seeks to explore the prevalence, correlates and potential reasons for rural dropout throughout the secondary education process. It brings together results from eight large-scale survey studies covering 24,931 rural secondary students across four provinces, as well as analysis of extensive interviews with 52 students from these same study sites. The results show that the cumulative dropout rate across all windows of secondary education may be as high as 63 per cent. Dropping out is significantly correlated with low academic performance, high opportunity cost, low socio-economic status and poor mental health. A model is developed to suggest that rural dropout is primarily driven by two mechanisms: rational cost-benefit analysis or impulsive, stress-induced decision making.
The surfactant cetyltrimethylammonium ion (CTA+) was confined within the galleries of montmorillonite (MMT) to obtain a series of organo-montmorillonites (C16-MMTs) through an ion-exchange intercalation reaction. The C16-MMT formed a single precipitate layer when CTA+ loading was 18.3 wt% but stratified at high loadings. The conformational disorder increased with increasing CTA+ loading. The upper precipitate was characterized by a larger gallery height and a higher surfactant loading in comparison with lower precipitate. The confined methylene chains adopted a lateral monolayer with a small percentage of conformation freedoms at CTA+ loading of 18.3 wt%. The intercalated methylene chains were arranged either in a lateral monolayer or in a tilted interdigitated bilayer at CTA+ loading of 24.7 wt% while in either a tilted interdigitated bilayer or a lateral bilayer at high CTA+ loadings. The different arrangements of methylene chains intercalated in the MMT galleries are believed to be the reason for the stratification.
Chemical element properties are generally classified in six groups: size, atomic number, electrochemical factor, valence-electron, cohesive energy, and angular valence-orbital. It is well known that some bulk properties of materials, like electrical conductivity and heat capacity of metals, may be interpreted in principle based on their constituent chemical element properties. In this study, effects of additives in galvanizing have been correlated to the chemical element properties of the additives. By screening all chemical elements (in the periodic table of elements) with this model, new additives, like Ca, Sc, Ge, Sr, and Y, have been predicted to reduce the steel weight loss in galvanizing. This model may also help to design new alloys as additives.
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