BACKGROUND Zero‐valent iron (ZVI) is an environmentally friendly and cost‐efficient material for the removal of contaminants. However, its application is challenged by problems such as surface passivation and pH increase in the reaction media. As a potential solution for this problem, pyrite, a waste of the mining industry, was used together with ZVI for reductive removal of azo dyes. RESULTS In comparison with ZVI alone, the ZVI/pyrite mixture showed dramatically enhanced efficiency and reusability for dye removal, although pyrite itself can hardly remove dyes. The reaction rate constant kobs for the removal of Orange II at the initial pH (pH0) of 7.0 was enhanced 9.4‐ to 28.0‐fold by using the mixture at a pyrite/ZVI mass ratio ranging from 1 to 4, respectively. The mechanism studies indicate that pyrite not only helped produce more iron(II) by facilitating iron corrosion and the iron(II)/iron(III) cycle in the mixed system, but also activated the ZVI surface by in situ sulfidation. The high reactivity of the adsorbed iron(II) in the mixed system significantly contributed to dye removal, especially at the alkalescent pH. CONCLUSION Pyrite accelerated the reductive removal of dyes by ZVI, through inhibiting its surface passivation and suppressing the pH increase in the reaction media. Therefore, this research proposes a facile method for enhancing the reactivity and applicability of ZVI in environmental remediation. © 2020 Society of Chemical Industry

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Ovarian age assessment is an important indicator to evaluate the ovarian reserve function and reproductive potential of women. At present, the application of ovarian age prediction model in China needs further improvement and optimization to make it more suitable for the actual situation of women in China. In this study, we collected subjects and their data in three ways: firstly, we collected clinical data from a number of women go to local hospital, including healthy women and women with DOR or PCOS; secondly, we obtained data by recruited healthy women through CRO companies for a fee; thirdly, we collected data from a number of healthy women using WeChat applet. Using the data collected by CRO company and WeChat applet, we applied the generalized linear model to optimize the ovarian age prediction model. The optimized formula is: OvAge = exp (3.5254–0.0001*PRL-0.0231*AMH), where P = 0.8195 for PRL and P = 0.0003 for AMH. Applying the formula to the hospital population data set for testing, it showed that the predicted ovarian age in the healthy women was comparable to their actual age, with a root mean squared error (RMSE) = 5.6324. The prediction accuracy was high. These data suggest that our modification of the ovarian age prediction model is feasible and that the formula is currently a more appropriate model for ovarian age assessment in healthy Chinese women. This study explored a new way to collect clinical data, namely, an online ovarian age calculator developed based on a WeChat applet, which can collect data from a large number of subjects in a short period of time and is more economical, efficient, and convenient. In addition, this study introduced real data to optimize the model, which could provide insights for model localization and improvement.

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