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Online since: May 2013
Authors: Xi Fang Zhu, Chen Lei, Xiao Jin, Xiong Chao, Zhang Yan, Hong Chun Yuan, Li Hua Ding, Xiang Cai Zhou
The results suggest the existence of a large number of interface states in ZnO/p-Si heterojunction, and the interface states can be reduced and the photoelectric properties can be further improved.
The preferential orientation of the ZnO grains is observed along the (101) axis aligning with the growth direction.
The presence of a number of peaks in XRD pattern is the indication of polycrystalline nature of the ZnO.
The preferential orientation of the ZnO grains is observed along the (101) axis aligning with the growth direction.
The presence of a number of peaks in XRD pattern is the indication of polycrystalline nature of the ZnO.
Online since: October 2024
Authors: Oleksandr Kireev, Dmytro Tregubov, Ilgar Dadashov, Olena Borsuk, Evgen Slepuzhnikov
Liquids modeling and their properties is carried out on the molecules coarse-grained model basis within the statistical associative theory by replacing the molecule with a solid balls limited number, by which fill the matrices [6], that allows predicting many substance properties and internal force fields.
The factor of 200 to the atoms number determines the electron density redistribution total path in the crystal (in contrast to the hydrocarbons linear structure).
In addition, in the equivalent length and molar mass, it is necessary to take into account a oxygen atoms certain number that could be cluster part as peroxide groups.
Methane requires 9.52 moles of air for the 1 mole combustion, for longer n-alkanes this number decreases to 7.3 per one molecular link.
Sanket, Development of New Transferable Coarse-Grained Models of Hydrocarbons, J.
The factor of 200 to the atoms number determines the electron density redistribution total path in the crystal (in contrast to the hydrocarbons linear structure).
In addition, in the equivalent length and molar mass, it is necessary to take into account a oxygen atoms certain number that could be cluster part as peroxide groups.
Methane requires 9.52 moles of air for the 1 mole combustion, for longer n-alkanes this number decreases to 7.3 per one molecular link.
Sanket, Development of New Transferable Coarse-Grained Models of Hydrocarbons, J.
Online since: May 2011
Authors: A.N. Ramakrishna, A.V. Pradeep Kumar, Keerthi Gowda
The possible training parameters
are number of iterations (epoch) learning rate, error goal and number of hidden layers.
However the numbers of neurons in the input layer and output layer are determined based on the problem domain depending up on number of input variables and number of output or target variables.
Tested: Properties tested BC Soil RHA Specific gravity 2.67 1.92 Liquid limit (%) 64.86 128.34 Plastic limit (%) 28.89 - Shrinkage limit (%) 11.48 - Plasticity index (%) 35.97 Non plastic Grain sizes Gravel fraction (%) - - Sand fraction (%) Coarse (%) 1.5 - Medium (%) 4 2.84 Fine (%) 12.54 31.45 Silt fraction (%) 26 52.35 Clay fraction (%) 55.96 13.36 MDD (KN/m2) 14.75 7.09 OMC (%) 26.53 74.24 Free swell index (%) 109.12 - Properties tested Value Grade 43 Specific gravity 3.12 Normal consistency (%) 32 Initial setting time (min) 96 Final setting time (min) 364 Fineness (%) 5.5 Compressive strength 3 days strength (Mpa) 25.5 7 days strength (Mpa) 36.7 28 days strength (Mpa) 41.5 Table 3 Chemical Properties of Soil Elements BC soil (%) RHA (%) SiO2 38.32 83.32 Fe2O2 2.69 0.8 SO3 0.034 0 CaO 3.05 0.71 MgO 2.69 0 Al2O3 5.93 0.8 Ignition loss 11.04 5.23 pH 8.06 9.69 Table 4 CBR of Soil-RHA-Cement
Mixes and RHA Mix proportion (%) Soaked CBR Soil RHA Cement (%) 100 0 0 1.88 (1.89) 95 5 0 4.08(4.08) 90 10 0 5.84(5.83) 85 15 0 5.14(5.36) 96 0 4 25.56(25.53) 92 0 8 48.57(48.53) 88 0 12 58.16(58.15) 91 5 4 28.16(28.18) 87 5 8 54.68(54.73) 83 5 12 63.09(63.04) 86 10 4 30.82(32.73) 82 10 8 60.56(59.11) 78 10 12 68.49(68.84) 81 15 4 25.85(25.85) 77 15 8 56.62(57.55) 73 15 12 67.06(67.34) Fig: 1 Illustrative topology to prognosticate CBR values BC soil- RHA-Cement mix Hidden layer CBR values Fig. 2 Effect of RHA and Cement on Soaked CBR Value of BC Soil Fig.3 Convergence curve The number of hidden layers and neurons in hidden layer are fixed during the training process.
A significant number of reports have been published in application of ANN for the prediction of future events in Civil engineering problems [1,2,3].
However the numbers of neurons in the input layer and output layer are determined based on the problem domain depending up on number of input variables and number of output or target variables.
Tested: Properties tested BC Soil RHA Specific gravity 2.67 1.92 Liquid limit (%) 64.86 128.34 Plastic limit (%) 28.89 - Shrinkage limit (%) 11.48 - Plasticity index (%) 35.97 Non plastic Grain sizes Gravel fraction (%) - - Sand fraction (%) Coarse (%) 1.5 - Medium (%) 4 2.84 Fine (%) 12.54 31.45 Silt fraction (%) 26 52.35 Clay fraction (%) 55.96 13.36 MDD (KN/m2) 14.75 7.09 OMC (%) 26.53 74.24 Free swell index (%) 109.12 - Properties tested Value Grade 43 Specific gravity 3.12 Normal consistency (%) 32 Initial setting time (min) 96 Final setting time (min) 364 Fineness (%) 5.5 Compressive strength 3 days strength (Mpa) 25.5 7 days strength (Mpa) 36.7 28 days strength (Mpa) 41.5 Table 3 Chemical Properties of Soil Elements BC soil (%) RHA (%) SiO2 38.32 83.32 Fe2O2 2.69 0.8 SO3 0.034 0 CaO 3.05 0.71 MgO 2.69 0 Al2O3 5.93 0.8 Ignition loss 11.04 5.23 pH 8.06 9.69 Table 4 CBR of Soil-RHA-Cement
Mixes and RHA Mix proportion (%) Soaked CBR Soil RHA Cement (%) 100 0 0 1.88 (1.89) 95 5 0 4.08(4.08) 90 10 0 5.84(5.83) 85 15 0 5.14(5.36) 96 0 4 25.56(25.53) 92 0 8 48.57(48.53) 88 0 12 58.16(58.15) 91 5 4 28.16(28.18) 87 5 8 54.68(54.73) 83 5 12 63.09(63.04) 86 10 4 30.82(32.73) 82 10 8 60.56(59.11) 78 10 12 68.49(68.84) 81 15 4 25.85(25.85) 77 15 8 56.62(57.55) 73 15 12 67.06(67.34) Fig: 1 Illustrative topology to prognosticate CBR values BC soil- RHA-Cement mix Hidden layer CBR values Fig. 2 Effect of RHA and Cement on Soaked CBR Value of BC Soil Fig.3 Convergence curve The number of hidden layers and neurons in hidden layer are fixed during the training process.
A significant number of reports have been published in application of ANN for the prediction of future events in Civil engineering problems [1,2,3].
Online since: October 2009
Authors: Shigeru Suzuki, Takamichi Yamamoto, Tomoya Uruga, Hajime Tanida, Hidenori Toyokawa, Yasuko Terada, Yasufumi Takagaki, Kozo Shinoda
Fig. 6 shows the pixel array number dependency of Mn K fluorescence intensities of the
three Fe-Mn alloy samples.
The number of detection pixel array was counted from the position at zero detection angle.
Therefore, the detection angle increases with increasing pixel array number.
As shown in Fig. 6(a), the fluorescence intensity distribution of the as-prepared sample increased monotonically with the number of pixel array (detection angle).
The roughness of the sample surfaces becomes larger with increasing the annealing temperature due to formation of manganese oxide and the grain growth at the surface.
The number of detection pixel array was counted from the position at zero detection angle.
Therefore, the detection angle increases with increasing pixel array number.
As shown in Fig. 6(a), the fluorescence intensity distribution of the as-prepared sample increased monotonically with the number of pixel array (detection angle).
The roughness of the sample surfaces becomes larger with increasing the annealing temperature due to formation of manganese oxide and the grain growth at the surface.
Online since: January 2006
Authors: Kun He Fang, Shu Hua Liu
It reduces the amount of Mg(OH)2, increase the area of the transition zone of grains, and
reduces the amount of Mg(OH)2 which plays an important part in the expansion of cement paste
Based on Table 1, Table 2 and Fig.2 can be further obtained: Table 2 Influence of Content of MgO on Autogenous Deformation Serial number P1 P8 P10 P13 M 0 3 5 6 k1 -16.8 37.3 61.1 77.1 - 40 - 20 0 20 40 60 80 100 0 2 4 6 8 M( %) k 1 417.14455.151 − = M k Fig. 2 Relationship between k1 and M Based on the relationship between influence coefficient of MgO on autogenous deformation and content of MgO, Eq.3 can be obtained to calculate the influence coefficient k1, as follows: 417.14455.151 − = M k .
The influence of the content of fly ash on autogenous deformation are showed by Table 3 and Fig.3: Table 3 Influence of Content of Fly Ash on Autogenous Deformation M(%) 20 25 30 40 Serial number P5 P10 P16 P18 G(×10-6 ) PiG 54.1 61.1 68.2 63.6 Serial number P7 P12 P17 P19 G(×10-6 ) PiG 122.4 112.8 103.7 88.6 k2(×10 -6 ) 88.3 87.0 86.0 72.1 74 76 78 80 82 84 86 88 90 0 10 20 30 40 50 F( %) k 2 74.72446.1034.0 2 2 ++−= F F k Fig. 3 Relationship between k2 and F Based on the relationship of the influence coefficient of the content of fly ash on the autogenous deformation (F), Eq.4 can be obtained to calculate the influence coefficient k2, as follows: 74.72446.1034.0 2 2 ++−= FF k .
Table 4 Influence of Temperature on Autogenous Deformation G(×10 -6) G(×10 -6) G(×10 -6) G(×10 -6) T (℃) Serial number PiG Serial number PiG Serial number PiG Serial number PiG respective ratio 20 P2 45.2 P5 54.1 P10 61.1 P13 77.1 59.4 30 P3 100.9 P6 115.1 P11 71.7 P14 123.7 102.9 40 P4 111.5 P7 122.4 P12 112.8 P15 146.9 123.4 Turning the ratio in Table 4 into k3, we get the influence coefficient of temperature on the autogenous deformation at the temperature of 20 ℃, 30 ℃ and 40 ℃.
Based on Table 1, Table 2 and Fig.2 can be further obtained: Table 2 Influence of Content of MgO on Autogenous Deformation Serial number P1 P8 P10 P13 M 0 3 5 6 k1 -16.8 37.3 61.1 77.1 - 40 - 20 0 20 40 60 80 100 0 2 4 6 8 M( %) k 1 417.14455.151 − = M k Fig. 2 Relationship between k1 and M Based on the relationship between influence coefficient of MgO on autogenous deformation and content of MgO, Eq.3 can be obtained to calculate the influence coefficient k1, as follows: 417.14455.151 − = M k .
The influence of the content of fly ash on autogenous deformation are showed by Table 3 and Fig.3: Table 3 Influence of Content of Fly Ash on Autogenous Deformation M(%) 20 25 30 40 Serial number P5 P10 P16 P18 G(×10-6 ) PiG 54.1 61.1 68.2 63.6 Serial number P7 P12 P17 P19 G(×10-6 ) PiG 122.4 112.8 103.7 88.6 k2(×10 -6 ) 88.3 87.0 86.0 72.1 74 76 78 80 82 84 86 88 90 0 10 20 30 40 50 F( %) k 2 74.72446.1034.0 2 2 ++−= F F k Fig. 3 Relationship between k2 and F Based on the relationship of the influence coefficient of the content of fly ash on the autogenous deformation (F), Eq.4 can be obtained to calculate the influence coefficient k2, as follows: 74.72446.1034.0 2 2 ++−= FF k .
Table 4 Influence of Temperature on Autogenous Deformation G(×10 -6) G(×10 -6) G(×10 -6) G(×10 -6) T (℃) Serial number PiG Serial number PiG Serial number PiG Serial number PiG respective ratio 20 P2 45.2 P5 54.1 P10 61.1 P13 77.1 59.4 30 P3 100.9 P6 115.1 P11 71.7 P14 123.7 102.9 40 P4 111.5 P7 122.4 P12 112.8 P15 146.9 123.4 Turning the ratio in Table 4 into k3, we get the influence coefficient of temperature on the autogenous deformation at the temperature of 20 ℃, 30 ℃ and 40 ℃.
Online since: September 2011
Authors: Ya Nan He
They are arranged as following with an implied means: sun, moon, stars, mountains, dragon, pheasant, two goblets, pondweed, flames, rice grains axe and bows.
In ancient China, the number nine and five usually symbolized nobility, so people adopted fabric redesign method, embroidered nine dragons’ pattern on emperor's robes, namely Dragon Robe.
So the Dragon Robe always stands for the extreme kingship, is a good example of using auspicious numbers to express symbolic implication through fabric embroidery redesign.[1] The embroidery patterns on the “patch” used on official costume in the Ming and Qing dynasty also contained profound symbolic meaning.
In ancient China, the number nine and five usually symbolized nobility, so people adopted fabric redesign method, embroidered nine dragons’ pattern on emperor's robes, namely Dragon Robe.
So the Dragon Robe always stands for the extreme kingship, is a good example of using auspicious numbers to express symbolic implication through fabric embroidery redesign.[1] The embroidery patterns on the “patch” used on official costume in the Ming and Qing dynasty also contained profound symbolic meaning.
Online since: August 2012
Authors: Lin Zhang, Zhi Zhong Dong, Yong Chang Liu, Ya Peng Wang, Zhi Chao Liu, Kang Li, Hua Jiang
During 4h to 16h, the size of VC particles enlarged obviously both in the grains and along the boundaries.
It could be attributed to that a large number of fine VC precipitates emerge uniformly before 4h aging as seen in Fig.3 (a) and (b), this inevitably causes matrix homogenization in component content dropping, which directly lead to degradation of corrosion resistance.
After 4h, the number of VC precipitates increases tardily as shown in Fig.3 (b), (c) and (d), but the precipitates grow obviously.
It could be attributed to that a large number of fine VC precipitates emerge uniformly before 4h aging as seen in Fig.3 (a) and (b), this inevitably causes matrix homogenization in component content dropping, which directly lead to degradation of corrosion resistance.
After 4h, the number of VC precipitates increases tardily as shown in Fig.3 (b), (c) and (d), but the precipitates grow obviously.
Online since: November 2011
Authors: Eirini Varouti
The parameter counts refers to the number of Barkhausen Emission pulses.
The number of pulses depends on the threshold voltage which can be selected by user in order to obtain results giving the most comprehensive information about material and stress.
The ferritic grains boundaries are decorated with bainitic ferrite and, finally, islands of retained austenite could be observed.
The number of pulses depends on the threshold voltage which can be selected by user in order to obtain results giving the most comprehensive information about material and stress.
The ferritic grains boundaries are decorated with bainitic ferrite and, finally, islands of retained austenite could be observed.
Online since: January 2004
Authors: A.F. Gualtieri, M. Dapiaggi, D. Levy, E. Belluso
Given
its outstanding properties, asbestos has been utilised since the ancient times for a large number of
applications.
Although a number of papers in the literature deal with the thermal transformation of amphibole minerals, they mainly describe the oxidation and related dehydrogenation and dehydroxylation reactions but none reports the kinetics of the decomposition reaction.
Further TEM investigations are necessary to confirm the proposed reaction mechanism and to help to clear the follwong issues: (1) the nucleation and growth of hematite: does it occur at the surface, grain (fibril) boundaries and/or in correspondence to structure defects?
Although a number of papers in the literature deal with the thermal transformation of amphibole minerals, they mainly describe the oxidation and related dehydrogenation and dehydroxylation reactions but none reports the kinetics of the decomposition reaction.
Further TEM investigations are necessary to confirm the proposed reaction mechanism and to help to clear the follwong issues: (1) the nucleation and growth of hematite: does it occur at the surface, grain (fibril) boundaries and/or in correspondence to structure defects?
Online since: November 2013
Authors: Md Akil Hazizan, Nosbi Norlin
Introduction
Carbon nanotubes (CNTs) have a unique structure of the rolled graphene sheets shape with a large number of hilicities and chiralities of graphene sheet rolled up like a scroll [1].
The catalyst was grained small before calcined in oven at 900ºC for 10 hours.
Fourier transform infrared (FTIR) spectra of the samples were performed in a scan range wave number of 4000-500 cm-1 with a resolution of 2 cm-1 at room temperature.
The catalyst was grained small before calcined in oven at 900ºC for 10 hours.
Fourier transform infrared (FTIR) spectra of the samples were performed in a scan range wave number of 4000-500 cm-1 with a resolution of 2 cm-1 at room temperature.