Vortex dynamics and second magnetization peak in PrFeAsO0.60_{0.60}F0.12_{0.12} superconductor

We have studied the vortex dynamics in the PrFeAsO$_{0.60}$F$_{0.12}$ superconducting sample by dc magnetization and dynamic magnetization-relaxation rate $(Q)$ measurements. The field dependence of the superconducting irreversible magnetization $M_s$ reveals a second magnetization peak or fishtail effect. The large value of $Q$ is an indication of moderate vortex motion and relatively weak pinning energy. Data analysis based on the generalized inversion scheme suggests that the vortex dynamics can be described by the collective pinning model. The temperature dependence of the critical current is consistent with the pinning due to the spatial variation in the mean free path near a lattice defect ($\delta l$ pinning). The temperature and field dependence of $Q$ indicates a crossover from elastic to plastic vortex creep with increasing temperature and magnetic field. Finally, we have constructed the vortex phase diagram based on the present data.Comment: 11 pages, 8 Figures, Accepted for publication in Journal of Applied Physic

Fe-spin reorientation in PrFeAsO : Evidences from resistivity and specific heat studies

We report the magnetic field dependence of resistivity ($\rho$) and specific heat ($C$) for the non-superconducting PrFeAsO compound. Our study shows a hitherto unobserved anomaly at $T_{SR}$ in the resistivity and specific heat data which arises as a result of the interplay of antiferromagnetic (AFM) Pr and Fe sublattices. Below the AFM transition temperature ($T_N^{\rm{Pr}}$), Pr moment orders along the crystallographic c axis and its effect on the iron subsystem causes a reorientation of the ordered inplane Fe moments in a direction out of the $ab$ plane. Application of magnetic field introduces disorder in the AFM Pr sublattice, which, in turn, reduces the out-of-plane Pr-Fe exchange interaction responsible for Fe spin reorientation. Both in $\rho$($T$) and $d(C/T)/dT$ curves, the peak at $T_{SR}$ broadens with the increase of $H$ due to the introduction of the disorder in the AFM Pr sublattice by magnetic field. In $\rho$($T$) curve, the peak shifts towards lower temperature with $H$ and disappears above 6 T while in $d(C/T)/dT$ curve the peak remains visible up to 14 T. The broadening of the anomaly at $T_N^{\rm{Pr}}$ in $C(T)$ with increasing $H$ further confirms that magnetic field induces disorder in the AFM Pr sublattice.Comment: 8 pages, 10 Figure

Quantm Magnetoresistance of the PrFeAsO oxypnictides

We report the observation of an unusual $B$ dependence of transverse magnetoresistance (MR) in the PrFeAsO, one of the parent compound of pnictide superconductors. Below the spin density wave transition, MR is large, positive and increases with decreasing temperature. At low temperatures, MR increases linearly with $B$ up to 14 T. For $T$$\geq$40 K, MR vs $B$ curve develops a weak curvature in the low-field region which indicates a crossover from $B$ linear to $B^2$ dependence as $B$$\rightarrow$0. The $B$ linear MR originates from the Dirac cone states and has been explained by the quantum mechanical model proposed by Abrikosov.Comment: accepted for publication in Appl. Phys. Let

Machine learning with ensemble stacking model for automated sleep staging using dual-channel EEG signal

Abstract Sleep staging is an important part of diagnosing the different types of sleep-related disorders because any discrepancies in the sleep scoring process may cause serious health problems such as misinterpretations of sleep patterns, medication errors, and improper diagnosis. The best way of analyzing sleep staging is visual interpretations of the polysomnography (PSG) signals recordings from the patients, which is a quite tedious task, requires more domain experts, and time-consuming process. This proposed study aims to develop a new automated sleep staging system using the brain EEG signals. Based on a new automated sleep staging system based on an ensemble learning stacking model that integrates Random Forest (RF) and eXtreme Gradient Boosting (XGBoosting). Additionally, this proposed methodology considers the subjects' age, which helps analyze the S1 sleep stage properly. In this study, both linear (time and frequency) and non-linear features are extracted from the pre-processed signals. The most relevant features are selected using the ReliefF weight algorithm. Finally, the selected features are classified through the proposed two-layer stacking model. The proposed methodology performance is evaluated using the two most popular datasets, such as the Sleep-EDF dataset (S-EDF) and Sleep Expanded-EDF database (SE-EDF) under the Rechtschaffen & Kales (R&K) sleep scoring rules. The performance of the proposed method is also compared with the existing published sleep staging methods. The comparison results signify that the proposed sleep staging system has an excellent improvement in classification accuracy for the six-two sleep states classification. In the S-EDF dataset, the overall accuracy and Cohen's kappa coefficient score obtained by the proposed model is (91.10%, 0.87) and (90.68%, 0.86) with inclusion and exclusion of age feature using the Fpz-Cz channel, respectively. Similarly, the Pz-Oz channel's performance is (90.56%, 0.86) with age feature and (90.11%, 0.86) without age feature. The performed results with the SE-EDF dataset using Fpz-Cz channel is (81.32%, 0.77) and (81.06%, 0.76), using Pz-Oz channel with the inclusion and exclusion of the age feature, respectively. Similarly the model achieved an overall accuracy of 96.67% (CT-6), 96.60% (CT-5), 96.28% (CT-4),96.30% (CT-3) and 97.30% (CT-2) for with 16 selected features using S-EDF database. Similarly the model reported an overall accuracy of 85.85%, 84.98%, 85.51%, 85.37% and 87.40% for CT-6 to CT-2 with 18 selected features using SE-EDF database

Anomalous thermal expansion of Sb2_2Te3_3 topological insulator

We have investigated the temperature dependence of the linear thermal expansion along the hexagonal c axis ($\Delta L$), in-plane resistivity ($\rho$), and specific heat ($C_p$) of the topological insulator Sb$_2$Te$_3$ single crystal. $\Delta L$ exhibits a clear anomaly in the temperature region 204-236 K. The coefficient of linear thermal expansion $\alpha$ decreases rapidly above 204 K, passes through a deep minimum at around 225 K and then increases abruptly in the region 225-236 K. $\alpha$ is negative in the interval 221-228 K. The temperature dependence of both $\alpha$ and $C_p$ can be described well by the Debye model from 2 to 290 K, excluding the region around the anomaly in $\alpha$

The magnetization of PrFeAsO0.60_{0.60}F$_{0.12} sueprconductor

The magnetization of the PrFeAsO$_{0.60}$F$_{0.12}$ polycrystalline sample has been measured as functions of temperature and magnetic field $(H)$. The observed total magnetization is the sum of a superconducting irreversible magnetization ($M_s$) and a paramagnetic magnetization ($M_p$). Analysis of dc susceptibility $\chi(T)$ in the normal state shows that the paramagnetic component of magnetization comes from the Pr$^{+3}$ magnetic moments. The intragrain critical current density $(J_L)$ derived from the magnetization measurement is large. The $J_L(H)$ curve displays a second peak which shifts towards the high-field region with decreasing temperature. In the low-field region, a plateau up to a field $H^*$ followed by a power law $H^{-5/8}$ behavior of $J_L(H)$ is the characteristic of the strong pinning. A vortex phase diagram for the present superconductor has been obtained from the magnetization and resistivity data.Comment: A revised version with modified title,8 pages, 7 figure