Spin-lattice coupling mediated giant magnetodielectricity across the spin reorientation in Ca2FeCoO5

The structural, phonon, magnetic, dielectric, and magneto dielectric responses of the pure bulk Brownmillerite compound Ca2FeCoO5 are reported. This compound showed giant magneto dielectric response (10%-24%) induced by strong spin-lattice coupling across its spin reorientation transition (150-250 K). The role of two Debye temperatures pertaining to differently coordinated sites in the dielectric relaxations is established. The positive giant magneto-dielectricity is shown to be a direct consequence of the modulations in the lattice degrees of freedom through applied external field across the spin reorientation transition. Our study illustrates novel control of magneto-dielectricity by tuning the spin reorientation transition in a material that possess strong spin lattice coupling.Comment: 7 pages, 12 figure

Comparison of metabolic effects of glimepride and sitagliptin with metformin in patients suffering from type 2 diabetes mellitus in a tertiary care hospital

Background: Diabetes mellitus (DM) is one of the major causes of mortality & morbidity, and patient’s with better control of glycaemic parameters have lesser chronic complications associated with it. Though monotherapy with metformin is first choice for T2DM but is effective in less than 50% of patient and they should be managed with two drug therapy. Both Glimepiride and Sitagliptin are effective with metformin but there has been no study done in this region hence, we planned to study comparison of effects of glimepiride and sitagliptin with metformin in patient of T2DM.Methods: This prospective, open-label, randomized study was done in all patient diagnosed with T2DM, not adequately managed by metformin alone. The patient was divided into two group G (Glimepiride with Metformin) and Group S (Sitagliptin with Metformin) and had a follow up at 3 and 6 months. The biochemical parameters were assessed at 12 weeks and 24 weeks.Results: The result of this study show that both glimepiride and sitagliptin with metformin significantly (p<0.05) lowered both the fasting blood sugar as well as postprandial blood glucose at 3 and 6 months. Glimepiride was more effective in lowering (p<0.05) the plasma glucose at 3 months but both the drugs had comparable result at 6 months. This study also showed that glycosylated haemoglobin was lowered in both groups at three and six months as compared to Day 0 (p<0.05), with glimepiride having better control of glycosylated haemoglobin at 3 months with both groups having comparable result at 6 months.Conclusions: To conclude, this study compared effects of sitagliptin and glimepiride on glycaemic parameters in patients of T2DM and found that both drugs had comparable results

Self-propulsion in 2D Confinement: Phoretic and Hydrodynamic Interactions

Chemically active Janus particles generate tangential concentration gradients along their surface for self-propulsion. Although this is well studied in unbounded domains, the analysis in biologically relevant environments such as confinements is scarce. In this work, we study the motion of a Janus sphere in weak confinement. The particle is placed at an arbitrary location, with an arbitrary orientation between the two walls. Using the method of reflections, we study the effect of confining planar boundaries on the phoretic and hydrodynamic interactions, and their consequence on the Janus particle dynamics. The dynamical trajectories are analyzed using phase diagrams for different surface coverage of activity and solute-particle interactions. In addition to near wall states such as `sliding' and `hovering', we demonstrate that accounting for two planar boundaries reveals two new states: channel-spanning oscillations and damped oscillations around the centerline, which were characterized as `scattering' or `reflection' by earlier analyses on single-wall interactions. Using phase diagrams, we highlight the differences in inert-facing and active-facing Janus particles. We also compare the dynamics of Janus particles with squirmers for contrasting the chemical interactions with hydrodynamic effects. Insights from the current work suggest that biological and artificial swimmers sense their surroundings through long-ranged interactions, that can be modified by altering the surface properties.Comment: To appear in European Physical Journal

Composition and Structure Based GGA Bandgap Prediction Using Machine Learning Approach

This study focuses on developing precise machine learning (ML) regression models for predicting energy bandgap values based on chemical compositions and crystal structures. The primary aim is to match the accuracy of predictions derived from GGA-PBE calculations and validate them through density functional theory (DFT)-based band structure calculations. We assessed eight standalone ML regression models, including AdaBoost, Bagging, CatBoost, LGBM, RF, DT, GB, and XGB. These models were analyzed for their ability to predict GGA-PBE bandgap values across diverse material structures and compositions, using a dataset containing bandgap values for 106,113 compounds. Additionally, we constructed four ensemble models using the stacking method and seven using the bagging method. These ensemble models incorporated RidgeCV and LassoCV to explore if ensemble techniques could enhance prediction accuracy. The dataset was divided into subsets of varying sizes: 10,000, 25,000, 50,000, and 100,000 entries. We determined feature importance through permutation techniques and established a correlation coefficient matrix using the Pearson correlation method. The Random Forest (RF) model emerged as the top performer among standalone models, achieving an R2 value of 0.943 and an RMSE value of 0.504 eV. Bagging regression demonstrated improved performance across different dataset sizes with streamlined feature selection. Ensemble models, particularly bagging, consistently outperformed standalone models, achieving the best R2 value of 0.948 and an RMSE value of 0.479 eV in the test dataset. Using the best-performing model, we predicted bandgap values for new half-Heusler compounds with 18 valence electron counts. These predictions were successfully validated using accurate DFT calculations. DFT calculations indicated that the newly predicted compounds are narrow bandgap semiconductors with dynamic stability.Comment: 17 pages, 17 figures, Research pape

Accelerating Discovery of Vacancy Ordered 18-Valence Electron Half-Heusler Compounds: A Synergistic Approach of Machine Learning and Density Functional Theory

In this study, we attempted to model vacancy ordered half Heusler compounds with 18 valence electron count (VHH) derived from 19 VEC compounds such as TiNiSb such that the compositions will be Ti0.75NiSb, Zr0.75NiSb and Hf0.75NiSb with semiconducting behavior. The main motivation is that such a vacancy-ordered phase not only introduces semi conductivity but also it disrupts the phonon conducting path in HH alloys and thus reduces the thermal conductivity and as a consequence enhances the thermoelectric figure of merit. In order to predict the formation energy ({\Delta}Hf) from composition and crystal structure we have used 4684 compounds for their {\Delta}Hf values are available in the material project database and trained a machine learning model with R2 value of 0.943. Using this trained model, we have predicted the {\Delta}Hf of a list of VHH. From the predicted database of VHH we have selected Zr0.75NiSb and Hf0.75NiSb to validate the machine learning prediction using accurate DFT calculation. The calculated {\Delta}Hf for these two compounds from DFT calculation are found to be comparable with our ML prediction. The calculated electronic and lattice dynamics properties show that these materials are narrow band gap semiconductors and are dynamically stable as their all-phonon dispersion curves are having positive frequencies. The calculated Seebeck coefficient, electrical conductivity as well as thermal conductivity, power factor and thermoelectric figure of merit are analyzed.Comment: 5 pages, 2 figures, conferenc