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Monocrystalline SnSe is one of the most promising thermoelectric materials with outstanding performance and a high abundance of constituting elements. However, polycrystalline SnSe, which is more robust for applications, only shows large figure‐of‐merit (zT) values in its high‐symmetry phase. Stabilizing the high‐symmetry phase at low temperatures can thus enhance the average zT value over a broad temperature range. In this work, the high‐symmetry rock‐salt SnSe phase is successfully obtained by alloying SnSe with AgVVI2 compounds (V = Sb, Bi; VI = Se, Te). These cubic SnSe phases show a unique portfolio of properties including a high optical dielectric constant, a large maximum of optical absorption, a large Born effective charge, and abnormal bond‐breaking behavior in laser‐assisted atom probe tomography. All of these characteristics are indicative of metavalent bonding. In contrast, the Pnma phase of SnSe employs covalent bonding. The enhanced symmetry at low temperatures is realized by tailoring chemical bonding. Concomitantly, zT near room temperature is increased by a factor of more than 10 from the pristine Pnma SnSe to Fm3¯${{\bar{3}}}$m SnSe alloys. This provides insights into the enhancement of the thermoelectric performance of SnSe and other chalcogenides over a broad temperature range by manipulating the chemical bonds. [ABSTRACT FROM AUTHOR]

Three new hetero-metallic CuII-LnIII complexes [(CuL)Gd(NO3)3(CH3OH)]n (1), [(CuL)Tb(NO3)3(H2O)]· [CuL] (2) and [(CuL)Dy(NO3)3(H2O)]·[CuL] (3) have been synthesized using a mono-nuclear Cu(II) complex, [CuL], of an unsymmetrically di-condensed N2O3 donor Schiff base ligand, N-(3-methoxysalicylidene)-N-(salicylidene)-1,2-ethylenediamine (H2L). Single crystal X-ray crystallography revealed that complex 1 is a nitrate bridged 1D chain of dinuclear Cu(II)-Gd(III) units whereas in 2 and 3, the dinuclear Cu(II)-Ln(III) units are co-crystallized with a [CuL] unit. The Ln(III) centers are nine coordinated with the geometry of a spherical capped square antiprism for Gd and spherical tricapped trigonal prism for Tb and Dy. The geometry of the Cu(II) center is distorted octahedral for complex 1 and distorted square planar for complexes 2 and 3. Temperature-dependent molar magnetic susceptibility measurements in 1-3 revealed the presence of overall ferromagnetic coupling between the Cu(II) and Ln(III) centers. Notably, field induced single-molecule magnet behavior was witnessed in the Tb(III) derivative (2). The ab initio calculations indicated that upon application of an external magnetic field, the tunneling in the ground state of complex 2 gets reduced and thereby field-induced SMM behaviour is observed. Besides, in the case of complex 1, BS-DFT calculations were carried out to gain further insights into the magnetic exchange coupling interactions between the Cu(II) and Gd(III) centers. [ABSTRACT FROM AUTHOR]

Machine learning (ML) and Artificial intelligence (AI) play pivotal roles in diverse domains, particularly in handling the vast influx of data in recent times. These technologies offer potential for more accurate and expedited decision-making, particularly in predicting diseases. A significant health concern is cardiovascular disease, a prominent factor contributing to mortality in recent times. Detecting heart disease in its early stages can significantly impact survival rates and quality of life. Prompt diagnosis is crucial, given the complexity and varied causes associated with this ailment. The inquiry primarily focuses on improving the accuracy of disease prognosis through the application of Machine Learning approaches like Support Vector Machine, Random Forest and XGBoost. A dedicated effort was made to refine the accuracy of SVM by fine-tuning hyperparameters through GridSearchCV. The comprehensive evaluation of these algorithms revealed that SVM achieved an accuracy of 80.22%, XGBoost exhibited 75.82% accuracy, while Random Forest notably outperformed both, boasting an accuracy of 85.71%. These results underscore the strong predictive abilities of Random Forest in detecting heart disease, outperforming the efficacy of the other models. The study's results highlight the capability of machine learning, particularly Random Forest, in forecasting heart disease. Collaborative efforts with medical professionals and ongoing research aim to further refine these models, pushing accuracy rates closer to 100%. This advancement could mark a significant breakthrough in machine learning-driven disease prediction algorithms. In summary, this study showcases the prognostic capacities of SVM, Random Forest, and XGBoost in heart disease prognosis. Emphasizing the superior performance of Random Forest, the findings advocate for its adoption in improving disease diagnostics and intervention strategies. [ABSTRACT FROM AUTHOR]

The present work includes the fabrication of pure BiFeO3 and Bi0.9Y0.1Fe1−xCuxO3 (x = 0.05, 0.10, 0.15) nanoparticles by the usual sol-gel route. The characterization of structural, morphological, optical, electrical, and magnetic properties of the synthesized samples was achieved through x-ray diffraction spectroscopy, Raman spectroscopy, Fourier transform infrared spectroscopy, scanning electron microscopy, an impedance analyzer, and a vibrating sample magnetometer. The Fourier transform infrared study confirmed the presence of Bi–O and Fe–O bonds. The x-ray diffraction patterns confirmed the rhombohedral (R3c) structure as a major phase with the orthorhombic (Pnma) structure as a secondary phase in all samples, and few amount of impurity phase of Bi25FeO40 was also determined. The surface morphology analysis showed the variation of particle size from 63 to 174 nm. The Raman spectra of 13 optical phonon modes, including 4A1 and 9E symmetric phonons, are observed for all samples, and the positions of these modes are almost similar, except the intensities of A1 modes. The optical measurements show a reduction in bandgap from 2.17 to 2.03 eV with co-doping, revealing itself as a more promising applicant in photovoltaics. An enhanced value of the dielectric constant is observed for different doped samples at different frequency regions from 100 Hz to 10 MHz. Magnetic investigation demonstrates the improved ferromagnetism with co-doping of Y and Cu due to distortion in the FeO6 octahedron. A maximum saturation magnetization of 0.26 emu/gm and a coercivity of 2915 Oe were found for Y concentration of 10% and Cu concentration of 15%. [ABSTRACT FROM AUTHOR]