Simultaneous effects of conduction band non-parabolicity and hydrostatic pressure on the binding energies of 1S, 2S, and 2P states along with diamagnetic susceptibility of an on-center hydrogenic impurity confined in ...Simultaneous effects of conduction band non-parabolicity and hydrostatic pressure on the binding energies of 1S, 2S, and 2P states along with diamagnetic susceptibility of an on-center hydrogenic impurity confined in typical GaAs/Alx- Ga1-x As spherical quantum dots are theoretically investigated using the matrix diagonalization method. In this regard, the effect of band non-parabolieity has been performed using the Luttinger-Kohn effective mass equation. The binding energies and the diamagnetic susceptibility of the hydrogenic impurity are computed as a function of the dot radius and different values of the pressure in the presence of conduction band non-parabolicity effect. The results we arrived at are as follows： the incorporation of the band edge non-parabolicity increases the binding energies and decreases the absolute value of the diamagnetic susceptibility for a given pressure and radius; the binding energies increase and the magnitude of the diamagnetic susceptibility reduces with increasing pressure.展开更多
For predicting the molar diamagnetic susceptibilities of inorganic compounds, a novel connectivity index ^mG based on adjacency matrix of molecular graphs and ionic parameter gi was proposed. The gi is defined as gi=...For predicting the molar diamagnetic susceptibilities of inorganic compounds, a novel connectivity index ^mG based on adjacency matrix of molecular graphs and ionic parameter gi was proposed. The gi is defined as gi=（ni^0.5-0.91）^4·xi^0.5｜Zi^0.5, where Zi, ni, xi are the valence, the outer electronic shell primary quantum number, and the electronegativity of atom i respectively. The good QSPR models for the molar diamagnetic susceptibilities can be constructed from ^0G and ^1G by using multivariate linear regression （MLR） method and artificial neural network （NN） method. The correlation coefficient r, standard error, and average absolute deviation of the MLR model and NN model are 0.9868, 5.47 cgs, 4.33 cgs, 0.9885, 5.09 cgs and 4.06 cgs, respectively, for the 144 inorganic compounds. The cross-validation by using the leave-one-out method demonstrates that the MLR model is highly reliable from the point of view of statistics. The average absolute deviations of predicted values of the molar diamagnetic susceptibility of other 62 inorganic compounds （test set） are 4.72 cgs and 4.06 cgs for the MLR model and NN model. The results show that the current method is more effective than literature methods for estimating the molar diamagnetic susceptibility of an inorganic compound. Both MLR and NN methods can provide acceptable models for the prediction of the molar diamagnetic susceptibilities. The NN model for the molar diamagnetic susceptibilities appears more reliable than the MLR model.展开更多
文摘Simultaneous effects of conduction band non-parabolicity and hydrostatic pressure on the binding energies of 1S, 2S, and 2P states along with diamagnetic susceptibility of an on-center hydrogenic impurity confined in typical GaAs/Alx- Ga1-x As spherical quantum dots are theoretically investigated using the matrix diagonalization method. In this regard, the effect of band non-parabolieity has been performed using the Luttinger-Kohn effective mass equation. The binding energies and the diamagnetic susceptibility of the hydrogenic impurity are computed as a function of the dot radius and different values of the pressure in the presence of conduction band non-parabolicity effect. The results we arrived at are as follows： the incorporation of the band edge non-parabolicity increases the binding energies and decreases the absolute value of the diamagnetic susceptibility for a given pressure and radius; the binding energies increase and the magnitude of the diamagnetic susceptibility reduces with increasing pressure.
基金Project supported by the University Natural Science Foundation of Jiangsu Province （No. 04KJD150195）. Acknowledgement The authors wish to express our gratitude to the referees for their value comments.
文摘For predicting the molar diamagnetic susceptibilities of inorganic compounds, a novel connectivity index ^mG based on adjacency matrix of molecular graphs and ionic parameter gi was proposed. The gi is defined as gi=（ni^0.5-0.91）^4·xi^0.5｜Zi^0.5, where Zi, ni, xi are the valence, the outer electronic shell primary quantum number, and the electronegativity of atom i respectively. The good QSPR models for the molar diamagnetic susceptibilities can be constructed from ^0G and ^1G by using multivariate linear regression （MLR） method and artificial neural network （NN） method. The correlation coefficient r, standard error, and average absolute deviation of the MLR model and NN model are 0.9868, 5.47 cgs, 4.33 cgs, 0.9885, 5.09 cgs and 4.06 cgs, respectively, for the 144 inorganic compounds. The cross-validation by using the leave-one-out method demonstrates that the MLR model is highly reliable from the point of view of statistics. The average absolute deviations of predicted values of the molar diamagnetic susceptibility of other 62 inorganic compounds （test set） are 4.72 cgs and 4.06 cgs for the MLR model and NN model. The results show that the current method is more effective than literature methods for estimating the molar diamagnetic susceptibility of an inorganic compound. Both MLR and NN methods can provide acceptable models for the prediction of the molar diamagnetic susceptibilities. The NN model for the molar diamagnetic susceptibilities appears more reliable than the MLR model.
