| 1 | Autotrophic denitrification for nitrate and nitrite removal using sulfur-limestone显示文摘Sulfur-limestone was used in the autotrophic denitrification process to remove the nitrate and nitrite in a lab scale upflow biofilter.Synthetic water with four levels of nitrate and nitrite concentrations of 10,40,70 and 100 mg N/L was tested.When treating the low concentration of nitrate-or nitrite-contaminated water(10,40 mg N/L),a high removal rate of about 90% was achieved at the hydraulic retention time(HRT) of 3 hr and temperature of 20-25°C.At the same HRT,50% of the nitrate or nitrite could be removed even at the low temperature of 5-10°C.For the higher concentration nitrate and nitrite(70,100 mg N/L),longer HRT was required.The batch test indicated that influent concentration,HRT and temperature are important factors afiecting the denitrification eficiency.Molecular analysis implied that nitrate and nitrite were denitrified into nitrogen by the same microorganisms.The sequential two-step-reactions from nitrate to nitrite and from nitrite to the next-step product might have taken place in the same cell during the autotrophic denitrification process. | Weili Zhou Yejue Sun Bingtao Wu Yue Zhang Min Huang Toshiaki Miyanaga Zhenjia Zhang | 2011 | Journal of Environmental Sciences2011，23，11： | 34 |
| 2 | Machine learning guided appraisal and exploration of phase design for high entropy alloys显示文摘High entropy alloys(HEAs)and compositionally complex alloys(CCAs)have recently attracted great research interest because of their remarkable mechanical and physical properties.Although many useful HEAs or CCAs were reported,the rules of phase design,if there are any,which could guide alloy screening are still an open issue.In this work,we made a critical appraisal of the existing design rules commonly used by the academic community with different machine learning(ML)algorithms.Based on the artificial neural network algorithm,we were able to derive and extract a sensitivity matrix from the ML modeling,which enabled the quantitative assessment of how to tune a design parameter for the formation of a certain phase,such as solid solution,intermetallic,or amorphous phase.Furthermore,we explored the use of an extended set of new design parameters,which had not been considered before,for phase design in HEAs or CCAs with the ML modeling.To verify our ML-guided design rule,we performed various experiments and designed a series of alloys out of the Fe-Cr-Ni-Zr-Cu system.The outcomes of our experiments agree reasonably well with our predictions,which suggests that the ML-based techniques could be a useful tool in the future design of HEAs or CCAs. | Ziqing Zhou Yeju Zhou Quanfeng He Zhaoyi Ding Fucheng Li Yong Yang | 2019 | npj Computational Materials2019，，1： | 16 |
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