The aim of the present study was to investigate the influence of heavy metals on the polymorph transformation of tricalcium silicate.Heavy metal（0.1wt% to 3.0wt%） of Cr,Zn,Cu,Ni and Pb（in oxides form） was added in...The aim of the present study was to investigate the influence of heavy metals on the polymorph transformation of tricalcium silicate.Heavy metal（0.1wt% to 3.0wt%） of Cr,Zn,Cu,Ni and Pb（in oxides form） was added into the raw mixtures and then sintered together three times at 1450 ℃ for 2 h.The f-CaO content of doped C3S was determined by the glycerol-ethanol method,and their polymorph transformation was investigated by means of XRD and FTIR.Thermal analysis（DTA/DTG） was conducted to determine the reaction temperatures and mass losses during the sintering process of raw mixtures.The concentration of heavy metal in C3S after sintering was determined by ICP-AES.The experimental results indicate that heavy metal doping contributes to a higher symmetry of C3S except for Pb.Addition of up to 3.0wt%,Cr will lead to a decomposition of C3S into C2S and CaO;Zn will cause a transformation from T1 to M2 polymorph,and then to R polymorph;Cu and Ni cause a gradual transformation from T1 to T2 and then to M1 and/or M2 polymorph.During the sintering process,all the Pb releases into atmosphere because of evaporation.展开更多
This study introduces a novel conditional recycle generative adversarial network for facial attribute transfor- mation, which can transform high-level semantic face attributes without changing the identity. In our app...This study introduces a novel conditional recycle generative adversarial network for facial attribute transfor- mation, which can transform high-level semantic face attributes without changing the identity. In our approach, we input a source facial image to the conditional generator with target attribute condition to generate a face with the target attribute. Then we recycle the generated face back to the same conditional generator with source attribute condition. A face which should be similar to that of the source face in personal identity and facial attributes is generated. Hence, we introduce a recycle reconstruction loss to enforce the final generated facial image and the source facial image to be identical. Evaluations on the CelebA dataset demonstrate the effectiveness of our approach. Qualitative results show that our approach can learn and generate high-quality identity-preserving facial images with specified attributes.展开更多
基金Funded by the National Natural Science Foundation of China(51002110)the Fundamental Research Funds for the Central Universities(2012-IV-025)the State Scholarship Program of China Scholarship Council
文摘The aim of the present study was to investigate the influence of heavy metals on the polymorph transformation of tricalcium silicate.Heavy metal（0.1wt% to 3.0wt%） of Cr,Zn,Cu,Ni and Pb（in oxides form） was added into the raw mixtures and then sintered together three times at 1450 ℃ for 2 h.The f-CaO content of doped C3S was determined by the glycerol-ethanol method,and their polymorph transformation was investigated by means of XRD and FTIR.Thermal analysis（DTA/DTG） was conducted to determine the reaction temperatures and mass losses during the sintering process of raw mixtures.The concentration of heavy metal in C3S after sintering was determined by ICP-AES.The experimental results indicate that heavy metal doping contributes to a higher symmetry of C3S except for Pb.Addition of up to 3.0wt%,Cr will lead to a decomposition of C3S into C2S and CaO;Zn will cause a transformation from T1 to M2 polymorph,and then to R polymorph;Cu and Ni cause a gradual transformation from T1 to T2 and then to M1 and/or M2 polymorph.During the sintering process,all the Pb releases into atmosphere because of evaporation.
基金This work was supported by the National Natural Science Foundation of China under Grant Nos. 61672520, 61573348, 61620106003, and 61720106006, the Beijing Natural Science Foundation of China under Grant No. 4162056, the National Key Technology Research and Development Program of China under Grant No. 2015BAH53F02, and the CASIA-Tencent YouTu Jointly Research Project. The Titan X used for this research was donated by the NVIDIA Corporation.
文摘This study introduces a novel conditional recycle generative adversarial network for facial attribute transfor- mation, which can transform high-level semantic face attributes without changing the identity. In our approach, we input a source facial image to the conditional generator with target attribute condition to generate a face with the target attribute. Then we recycle the generated face back to the same conditional generator with source attribute condition. A face which should be similar to that of the source face in personal identity and facial attributes is generated. Hence, we introduce a recycle reconstruction loss to enforce the final generated facial image and the source facial image to be identical. Evaluations on the CelebA dataset demonstrate the effectiveness of our approach. Qualitative results show that our approach can learn and generate high-quality identity-preserving facial images with specified attributes.
