arXiv subjects

Achyut Tiwari

Publications and source records attributed to Achyut Tiwari.

6 recordsLinked to original sources

arXiv2025

Unconventional anisotropic charge dynamics in bulk $1T$-TaS$_2$ induced by interlayer dimerization

The commensurate charge-density-wave phase of the prototypical transition metal dichalcogenide $1T$-TaS$_2$ is investigated by temperature- and polarization-dependent infrared spectroscopy, revealing distinct charge dynamics parallel and perpendicular to the layers. Supported by density-functional-theory calculations, we show that the in-plane electronic structure in the low-temperature commensurate phase is reconstructed by the $\sqrt{13}\times\sqrt{13}$ distortion of the Ta layers. In contrast, the out-of-plane response is governed by a quasi-one-dimensional, Peierls-like dimerization of the two-dimensional star-of-David layers. Our results identify this dimerization as the dominant mechanism of the metal-to-insulator transition in both directions, ruling out a significant role of electronic correlations.

cond-mat.str-el↗

arXiv2025

Interlayer coupling driven phase evolution in hyperbolic $1T$-TaS$_2$

Understanding how microscopic interactions control macroscopic phase transitions is central to quantum materials, where charge density waves (CDWs), Mott states, and superconductivity often compete. In $1T$-TaS$_2$, this competition is tied to a sequence of CDW phases and a hysteretic metal-insulator transition, but details of the transition, especially the role of interlayer coupling, remain unresolved. In this work, spectroscopic ellipsometry is used to determine the uniaxial dielectric response of bulk $1T$-TaS$_2$ from room temperature down to the commensurate insulating state. The room-temperature data reveal natural type-II hyperbolic behavior in the visible range, with negative in-plane and positive out-of-plane permittivity. Temperature-dependent ellipsometry combined with anisotropic Bruggeman effective medium analysis shows that the metallic domains responsible for percolation evolve from disc-like to needle-like shapes, and that, upon heating, an additional intermediate phase emerges. These results identify the transition in $1T$-TaS$_2$ as a three-dimensional, interlayer-driven percolation process and establish this material as a natural, tunable hyperbolic medium.

cond-mat.str-el↗

arXiv2025

How to Fix Silver for Plasmonics

Silver (Ag) is considered an ideal material for plasmonic applications in the visible wavelength regime due to its superior optical properties, but its use is limited by the poor chemical stability and structural quality of thermally evaporated thin films and resulting nanostructures. In this study, we present a simple approach to enhance the structural and optical quality as well as the chemical stability of Ag thin films by alloying with gold (Au) through thermal co-evaporation. We investigate Ag$_{100-x}$Au$_x$ thin films with Au contents ranging from 5 to 20 at% analyzing their surface morphology, crystallite structure, optical properties, and chemical stability. Our results show that low Au concentrations significantly reduce the roughness of co-evaporated thin films (down to 0.4 nm RMS), and significantly enhance the resistance to oxidation, while maintaining a defined crystallite growth. Importantly, these improvements are achieved without the need for template stripping, metallic wetting layers, or epitaxial substrates, enabling direct deposition on glass. Among the compositions studied, Ag$_{95}$Au$_5$ thin films exhibit the highest chemical stability, lowest optical losses in the visible spectral range, and excellent plasmonic properties even outcompeting pure Ag. As a proof-of-concept, we fabricate high-quality Ag$_{95}$Au$_5$ optical antennas that exhibit long-term durability under ambient conditions. Our approach provides a practical solution to overcome the limitations of Ag for plasmonic device applications.

cond-mat.mtrl-sci↗

arXiv2024

Temperature-dependent generalized ellipsometry of the metal-insulator phase transition in low-symmetry charge-transfer salts

Determining the optical and electronic properties of strongly anisotropic materials with symmetries below orthorhombic remains challenging; generalized ellipsometry is a powerful technique in this regard. Here, we employ Mueller matrix spectroscopic and temperature-dependent ellipsometry to determine the frequency dependence of six components of the dielectric-function tensor of the two-dimensional charge-transfer salt $α$-(BEDT-TTF)$_2$I$_3$ across its metal-insulator transition. Our results offer valuable insights into temperature-dependent changes of the components of the spectroscopic dielectric-function tensor across the metal-insulator transition. This advanced method allows extension to other electronic transitions.

cond-mat.str-el↗

arXiv2024

Fermi-liquid behavior of non-altermagnetic RuO$_2$

Presence of magnetism in potentially altermagnetic RuO$_2$ has been a subject of intense debate. Using broadband infrared spectroscopy combined with density-functional band-structure calculations, we show that optical conductivity of RuO$_2$, the bulk probe of its electronic structure, is well described by the nonmagnetic model of this material. The sharp Pauli edge demonstrates the presence of a Dirac nodal line lying 45 meV below the Fermi level. Good match between the experimental and ab initio plasma frequencies underpins weakness of electronic correlations. The intraband part of the optical conductivity indicates Fermi-liquid behavior with two distinct scattering rates below 150 K. Fermi-liquid theory also accounts for the temperature-dependent magnetic susceptibility of RuO$_2$ and allows a consistent description of this material as paramagnetic metal.

cond-mat.mtrl-sci↗

arXiv2023

Ensemble Framework for Cardiovascular Disease Prediction

Heart disease is the major cause of non-communicable and silent death worldwide. Heart diseases or cardiovascular diseases are classified into four types: coronary heart disease, heart failure, congenital heart disease, and cardiomyopathy. It is vital to diagnose heart disease early and accurately in order to avoid further injury and save patients' lives. As a result, we need a system that can predict cardiovascular disease before it becomes a critical situation. Machine learning has piqued the interest of researchers in the field of medical sciences. For heart disease prediction, researchers implement a variety of machine learning methods and approaches. In this work, to the best of our knowledge, we have used the dataset from IEEE Data Port which is one of the online available largest datasets for cardiovascular diseases individuals. The dataset isa combination of Hungarian, Cleveland, Long Beach VA, Switzerland & Statlog datasets with important features such as Maximum Heart Rate Achieved, Serum Cholesterol, Chest Pain Type, Fasting blood sugar, and so on. To assess the efficacy and strength of the developed model, several performance measures are used, such as ROC, AUC curve, specificity, F1-score, sensitivity, MCC, and accuracy. In this study, we have proposed a framework with a stacked ensemble classifier using several machine learning algorithms including ExtraTrees Classifier, Random Forest, XGBoost, and so on. Our proposed framework attained an accuracy of 92.34% which is higher than the existing literature.

cs.LG↗