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您的检索式：作者名="S.Chakravarty"
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| 1 | 水介质中多金属海水矿瘤砷吸附的研究显示文摘本文详细报告了水介质中多金属海水矿瘤吸附砷(Ⅲ)和砷(Ⅴ)的研究结果。海水矿瘤的元素成分主要由铁、锰、硅(含有微量铝)、铜、钴和镍。海水矿瘤对砷(Ⅴ)的吸附取决于水介质的pH值,而砷(Ⅲ)的吸附不受水介质pH值的影响。砷吸附数据大体上符合兰米尔(Langmuir)等温线。砷(Ⅲ)和砷(Ⅴ)的动力学数据满足一种假定的二级动力学模型。海水矿瘤去除砷的效果取决于砷的初始浓度。当水介质中砷(Ⅲ)的初始浓度为0.34mg/L或砷(Ⅴ)的初始浓度为0.78mg/L时,海水矿瘤的最佳剂量为0.74mg/g。砷(Ⅲ)的吸附一般取决于离子环境。除PO4^3-以外,砷(Ⅲ)的吸附不受阴离子的影响,但受阳离子的影响显著。另一方面,砷(Ⅴ)的吸附受阴离子的影响显著,但不受阳离子的影响。试验结果表明,海水矿瘤吸附的砷(Ⅲ)主要为内部结核复合物,而吸附的砷(Ⅴ)为部分内部结核和部分外部结核复合物。当水介质的pH值为2-10时,吸附的砷(Ⅲ)和砷(Ⅴ)的解吸附较小。当水介质的pH值为6或更高时,海水矿瘤能被用于吸附地下水中的砷(Ⅲ)和砷(Ⅴ)物种。海水矿瘤被成功地用于去除采于印度西孟加拉邦的6种受砷污染地下水样中的砷(6种地下水样中砷的浓度范围为0.04-0.18mg/L)。 | S.Maity S.Chakravarty S.Bhattacharjee B.C. Roy 赵玉军(翻译) | 2006 | 水文地质工程地质技术方法动态2006，，4： | 0 |
| 2 | A deep neural network regressor for phase constitution estimation in the high entropy alloy system Al-Co-Cr-Fe-Mn-Nb-Ni显示文摘High Entropy Alloys(HEAs)are composed of more than one principal element and constitute a major paradigm in metals research.The HEA space is vast and an exhaustive exploration is improbable.Therefore,a thorough estimation of the phases present in the HEA is of paramount importance for alloy design.Machine Learning presents a feasible and non-expensive method for predicting possible new HEAs on-the-fly.A deep neural network(DNN)model for the elemental system of:Mn,Ni,Fe,Al,Cr,Nb,and Co is developed using a dataset generated by high-throughput computational thermodynamic calculations using Thermo-Calc.The features list used for the neural network is developed based on literature and freely available databases.A feature significance analysis matches the reported HEAs phase constitution trends on elemental properties and further expands it by providing so far-overlooked features.The final regressor has a coefficient of determination(r^(2))greater than 0.96 for identifying the most recurrent phases and the functionality is tested by running optimization tasks that simulate those required in alloy design.The DNN developed constitutes an example of an emulator that can be used in fast,real-time materials discovery/design tasks. | G.Vazquez S.Chakravarty R.Gurrola R.Arróyave | 2023 | npj Computational Materials2023，，1： | 0 |
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