This paper presents a versatile method for synthesizing electron-rich polynuclear transition metal clusters with chalcogen bridges and phosphine ligands.The reactions of transition metal complexes(R3P)2MX2(M=Co,Ni;R=P...This paper presents a versatile method for synthesizing electron-rich polynuclear transition metal clusters with chalcogen bridges and phosphine ligands.The reactions of transition metal complexes(R3P)2MX2(M=Co,Ni;R=Ph,Bu,Et;X=Cl,Br) with bridging reagents Na2Ex (E=S,Se;x=1.2) are described.The geometric and electronic structures of a series of polynuclear transition metal clusters with trianglar M3 units are also discussed.展开更多
The formation of borides M_3B_2 M_2B_2 and M_(23)B_6 may be carried out from the melt-quenched Fe_(70)Cr_(18)Mo_2SiB_9 during 700—1150℃ annealing.As the temperature raising,the M_2B,the majority being Fe_2B.may be g...The formation of borides M_3B_2 M_2B_2 and M_(23)B_6 may be carried out from the melt-quenched Fe_(70)Cr_(18)Mo_2SiB_9 during 700—1150℃ annealing.As the temperature raising,the M_2B,the majority being Fe_2B.may be gradually replaced by Cr_2B via the co-existence between Fe_2B and Cr_2B.The Cr_2B may be formed by trans formation of Fe_2B through the atomic substitu- tion and structural adjustment.The thin slice of remaining Fe_2B is sandwiched between(100) faces of(Cr,Fe)_2B as stacking fault.The M_2B_2 is virtually composed of the Mo_(1+x)(Fe,Cr)_(2-x)B_2 where x(0≤x≤1)increases with the increase of temperature.展开更多
Thermo-Calc software package (TCP+DICTRA) was used to simulate the carbide transformation process in die steel for plastic. Combined with TEM analysis, the calculated result confirms that the carbide in equilibrium st...Thermo-Calc software package (TCP+DICTRA) was used to simulate the carbide transformation process in die steel for plastic. Combined with TEM analysis, the calculated result confirms that the carbide in equilibrium state is M_7C_3 carbide. The dissolution of M_7C_3 carbide in steel is predicted by DICTRA program. It was shown that the temperature remarkably affects the dissolution process of M_7C_3 carbide, but the influence of alloy elements such as manganese and molybdenum can be neglected in this steel.展开更多
Recent years have witnessed a continuous discovering of new thermoelectric materials which has experienced a paradigm shift from try-and-error efforts to experience-based discovering and first-principles calculation. ...Recent years have witnessed a continuous discovering of new thermoelectric materials which has experienced a paradigm shift from try-and-error efforts to experience-based discovering and first-principles calculation. However, both the experiment and first-principles calculation deriving routes to determine a new compound are time and resources consuming. Here, we demonstrated a machine learning approach to discover new M_(2)X_(3)-type thermoelectric materials with only the composition information. According to the classic Bi_(2)Te_(3) material, we constructed an M_(2)X_(3)-type thermoelectric material library with 720 compounds by using isoelectronic substitution, in which only 101 compounds have crystalline structure information in the Inorganic Crystal Structure Database(ICSD) and Materials Project(MP) database. A model based on the random forest(RF) algorithm plus Bayesian optimization was used to explore the underlying principles to determine the crystal structures from the known compounds. The physical properties of constituent elements(such as atomic mass, electronegativity, ionic radius) were used to define the feature of the compounds with a general formula ^(1)M^(2)M^(1)X^(2)X^(3)X(^(1)M +^(2)M:^(1)X +^(2)X+^(3)X = 2:3). The primary goal is to find new thermoelectric materials with the same rhombohedral structure as Bi_(2)Te_(3) by machine learning.The final trained RF model showed a high accuracy of 91% on the prediction of rhombohedral compounds. Finally, we selected four important features to proceed with the polynomial fitting with the prediction results from the RF model and used the acquired polynomial function to make further discoveries outside the pre-defined material library.展开更多
文摘This paper presents a versatile method for synthesizing electron-rich polynuclear transition metal clusters with chalcogen bridges and phosphine ligands.The reactions of transition metal complexes(R3P)2MX2(M=Co,Ni;R=Ph,Bu,Et;X=Cl,Br) with bridging reagents Na2Ex (E=S,Se;x=1.2) are described.The geometric and electronic structures of a series of polynuclear transition metal clusters with trianglar M3 units are also discussed.
文摘The formation of borides M_3B_2 M_2B_2 and M_(23)B_6 may be carried out from the melt-quenched Fe_(70)Cr_(18)Mo_2SiB_9 during 700—1150℃ annealing.As the temperature raising,the M_2B,the majority being Fe_2B.may be gradually replaced by Cr_2B via the co-existence between Fe_2B and Cr_2B.The Cr_2B may be formed by trans formation of Fe_2B through the atomic substitu- tion and structural adjustment.The thin slice of remaining Fe_2B is sandwiched between(100) faces of(Cr,Fe)_2B as stacking fault.The M_2B_2 is virtually composed of the Mo_(1+x)(Fe,Cr)_(2-x)B_2 where x(0≤x≤1)increases with the increase of temperature.
文摘Thermo-Calc software package (TCP+DICTRA) was used to simulate the carbide transformation process in die steel for plastic. Combined with TEM analysis, the calculated result confirms that the carbide in equilibrium state is M_7C_3 carbide. The dissolution of M_7C_3 carbide in steel is predicted by DICTRA program. It was shown that the temperature remarkably affects the dissolution process of M_7C_3 carbide, but the influence of alloy elements such as manganese and molybdenum can be neglected in this steel.
基金the National Key Research and Development Program of China (No. 2018YFB0703600)Shenzhen Key Projects of Long-Term Support Plan (No. 20200925164021002)。
文摘Recent years have witnessed a continuous discovering of new thermoelectric materials which has experienced a paradigm shift from try-and-error efforts to experience-based discovering and first-principles calculation. However, both the experiment and first-principles calculation deriving routes to determine a new compound are time and resources consuming. Here, we demonstrated a machine learning approach to discover new M_(2)X_(3)-type thermoelectric materials with only the composition information. According to the classic Bi_(2)Te_(3) material, we constructed an M_(2)X_(3)-type thermoelectric material library with 720 compounds by using isoelectronic substitution, in which only 101 compounds have crystalline structure information in the Inorganic Crystal Structure Database(ICSD) and Materials Project(MP) database. A model based on the random forest(RF) algorithm plus Bayesian optimization was used to explore the underlying principles to determine the crystal structures from the known compounds. The physical properties of constituent elements(such as atomic mass, electronegativity, ionic radius) were used to define the feature of the compounds with a general formula ^(1)M^(2)M^(1)X^(2)X^(3)X(^(1)M +^(2)M:^(1)X +^(2)X+^(3)X = 2:3). The primary goal is to find new thermoelectric materials with the same rhombohedral structure as Bi_(2)Te_(3) by machine learning.The final trained RF model showed a high accuracy of 91% on the prediction of rhombohedral compounds. Finally, we selected four important features to proceed with the polynomial fitting with the prediction results from the RF model and used the acquired polynomial function to make further discoveries outside the pre-defined material library.
