Interface states of laser ablated BaTiO3 and Ba0.9Ca0.1TiO3 thin films in MFS structure determined by DLTS and C – V technique

BaTiO3 and Ba0.9Ca0.1TiO3 thin films were deposited on the p – type Si substrate by pulsed excimer laser ablation technique. The Capacitance – Voltage (C-V) measurement measured at 1 MHz exhibited a clockwise rotating hysteresis loop with a wide memory window for the Metal – Ferroelectric – Semiconductor (MFS) capacitor confirming the ferroelectric nature. The low frequency C – V measurements exhibited the response of the minority carriers in the inversion region while at 1 MHz the C – V is of a high frequency type with minimum capacitance in the inversion region. The interface states of both the MFS structures were calculated from the Castagne – Vaipaille method (High – low frequency C – V curve). Deep Level Transient Spectroscopy (DLTS) was used to analyze the interface traps and capture cross section present in the MFS capacitor. There were distinct peaks present in the DLTS spectrum and these peaks were attributed to the presence of the discrete interface states present at the semiconductor – ferroelectric interface. The distribution of calculated interface states were mapped with the silicon energy band gap for both the undoped and Ca doped BaTiO3 thin films using both the C – V and DLTS method. The interface states of the Ca doped BaTiO3 thin films were found to be higher than the pure BaTiO3 thin films.

Dynamics of water at the interface of a small protein, enterotoxin

Fully atomistic molecular dynamics simulations have been carried out to investigate the correlation of biological activity with dynamics of water molecules in an aqueous protein solution of the toxic domain of enterotoxin (PDB ID: 1ETN). This is a small protein of 13 amino acid residues. Our study of this water soluble protein clearly reveals that water dynamics slows down in the hydration layer. Despite this general slowing down, water molecules in the vicinity of the second beta turn of this protein exhibit faster dynamics than those near other regions of the protein. Since this beta turn is believed to play a critical role in the receptor binding of this protein, the faster dynamics of water near the beta turn m ay have biological significance. The collective orientational dynamics of the water molecules in the protein solution exhibits a characteristic long time component of 27 ps, which agrees well with dielectric relaxation experiments.

Reduction of oxygen impurity at GaN/beta-Si(3)N(4)/Si interface via SiO(2) to Ga(2)O conversion by exposing of Si surface under Ga flux

The removal of native oxide from Si (1 1 1) surfaces was investigated by X-ray photoelectron spectroscopy (XPS) and secondary ion mass spectra (SIMS) depth profiles. Two different oxide removal methods, performed under ultrahigh-vacuum (UHV) conditions, were carried out and compared. The first cleaning method is thermal desorption of oxide at 900 degrees C. The second method is the deposition of metallic gallium followed by redesorption. A significant decrease in oxygen was achieved by thermal desorption at 900 degrees C under UHV conditions. By applying a subsequent Ga deposition/redesorption, a further reduction in oxygen could be achieved. We examine the merits of an alternative oxide desorption method via conversion of the stable SiO(2) surface oxide into a volatile Ca(2)O oxide by a supply of Ga metals. Furthermore, ultra thin films of pure silicon nitride buffer layer were grown on a Si (1 1 1) surface by exposing the surface to radio-frequency (RF) nitrogen plasma followed by GaN growth. The SIMS depth profile shows that the oxygen impurity can be reduced at GaN/beta-Si(3)N(4)/Si interfaces by applying a subsequent Ga deposition/redesorption. (C) 2011 Elsevier B.V. All rights reserved.

Role of interface states associated with transitional nanophase precipitates in the photoluminescence enhancement of SrTiO3 : Pr3+,Al3+

SrTiO3:Pr3+,Al3+ phosphor samples with varying ratios of Sr/Ti/Al were prepared by the gel-carbonate method and the mechanism of enhancement of the red photoluminescence intensity therein was investigated. The photoluminescence (PL) spectra of SrTiO3:Pr3+ show both D-1(2) --> H-3(4) and P-3(0) --> H-3(4) emission in the red and blue spectral regions, respectively, with comparable intensity. The emission intensity of D-1(2) --> H-3(4) is drastically enhanced by the incorporation of Al3+ and excess Ti4+ in the compositional range Sr(Ti,Al-y)(O3+3y/2):Pr3+ (0.2 less than or equal to y less than or equal to 0.4) and SrTi1+xAlyO3+z:Pr3+ (0.2 less than or equal to x less than or equal to 0.5; 0.05 less than or equal to y less than or equal to 0.1; z = 2x + 3y/2) with the complete disappearance of the blue band. This cannot be explained by the simple point defect model as the EPR studies do not show any evidence for the presence of electron or hole centers. TEM investigations show the presence of exsolved nanophases of SrAl12O19 and/or TiO2 in the grain boundary region as well as grain interiors as lamellae which, in turn, form the solid-state defects, namely, dislocation networks, stacking faults and crystallographic shear planes whereby the framework of corner shared TiO6 octehedra changes over to edge-sharing TiO5-AlO5 strands as indicated from the Al-27 MAS NMR studies. The presence of transitional nanophases and the associated defects modify the excitation-emission processes by way of formation of electronic sub-levels at 3.40 and 4.43 eV, leading to magnetic-dipole related red emission with enhanced intensity. This is evidenced by the fact that SrAl12O19:Pr3+,Ti4+ shows bright red emission whereas SrAl12O19:Pr3+ does not show red photoluminescence.

A comparative study of ordinary kriging and support vector machine models for the spatial variability of rock depth in Bangalore

In this paper, reduced level of rock at Bangalore, India is arrived from the 652 boreholes data in the area covering 220 sq.km. In the context of prediction of reduced level of rock in the subsurface of Bangalore and to study the spatial variability of the rock depth, ordinary kriging and Support Vector Machine (SVM) models have been developed. In ordinary kriging, the knowledge of the semivariogram of the reduced level of rock from 652 points in Bangalore is used to predict the reduced level of rock at any point in the subsurface of Bangalore, where field measurements are not available. A cross validation (Q1 and Q2) analysis is also done for the developed ordinary kriging model. The SVM is a novel type of learning machine based on statistical learning theory, uses regression technique by introducing e-insensitive loss function has been used to predict the reduced level of rock from a large set of data. A comparison between ordinary kriging and SVM model demonstrates that the SVM is superior to ordinary kriging in predicting rock depth.

Multiclass Core Vector Machine

Even though several techniques have been proposed in the literature for achieving multiclass classification using Support Vector Machine(SVM), the scalability aspect of these approaches to handle large data sets still needs much of exploration. Core Vector Machine(CVM) is a technique for scaling up a two class SVM to handle large data sets. In this paper we propose a Multiclass Core Vector Machine(MCVM). Here we formulate the multiclass SVM problem as a Quadratic Programming(QP) problem defining an SVM with vector valued output. This QP problem is then solved using the CVM technique to achieve scalability to handle large data sets. Experiments done with several large synthetic and real world data sets show that the proposed MCVM technique gives good generalization performance as that of SVM at a much lesser computational expense. Further, it is observed that MCVM scales well with the size of the data set.

Single-machine scheduling with past-sequence-dependent setup times and learning effects: a parametric analysis

In this article, we consider the single-machine scheduling problem with past-sequence-dependent (p-s-d) setup times and a learning effect. The setup times are proportional to the length of jobs that are already scheduled; i.e. p-s-d setup times. The learning effect reduces the actual processing time of a job because the workers are involved in doing the same job or activity repeatedly. Hence, the processing time of a job depends on its position in the sequence. In this study, we consider the total absolute difference in completion times (TADC) as the objective function. This problem is denoted as 1/LE, (Spsd)/TADC in Kuo and Yang (2007) ('Single Machine Scheduling with Past-sequence-dependent Setup Times and Learning Effects', Information Processing Letters, 102, 22-26). There are two parameters a and b denoting constant learning index and normalising index, respectively. A parametric analysis of b on the 1/LE, (Spsd)/TADC problem for a given value of a is applied in this study. In addition, a computational algorithm is also developed to obtain the number of optimal sequences and the range of b in which each of the sequences is optimal, for a given value of a. We derive two bounds b* for the normalising constant b and a* for the learning index a. We also show that, when a < a* or b > b*, the optimal sequence is obtained by arranging the longest job in the first position and the rest of the jobs in short processing time order.