ArticleScientific reports2025
The influence of frequency and temperature on the AC-conductivity in [Formula: see text] semiconductor single crystal.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Elucidating the role of chromium substitution at the iron sites on the structural evolution and electrical performance of copper-based spinel ferrites.RSC advances · 2026Article
- Bridging laboratory findings and artificial intelligence for the design of TlInTeScientific reports · 2026Article
- Spectroscopic Evaluation of AlN/n-Si MIS Structures through Frequency-Driven Dielectric Characterization.ACS omega · 2026Article
- Organic-inorganic copper(ii)-based perovskite: a low-toxic, highly stable light absorber for optoelectronic applications.RSC advances · 2026Article
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Abstract
A special design, based on the Bridgman technique, was used in our laboratory for preparing single crystals of [Formula: see text]. The structure of [Formula: see text] in powder form was examined using X-ray diffraction. [Formula: see text] at room temperature was found to be a tetragonal system with lattice parameters of [Formula: see text] Å and [Formula: see text] Å. The structural parameters, such as crystallite size D, micro strain ε, dislocation density δ, and unit cell parameters were determined from XRD spectra. Thermo gravimetric analysis (TGA) was employed to study the thermal behavior of [Formula: see text], showcasing its significance in solid state physics. The TGA curve of [Formula: see text] exhibited distinct weight loss events corresponding to thermal decomposition processes. The frequency and temperature dependence of Ac-conductivity in a [Formula: see text] single crystal was studied by assessing the permittivity ([Formula: see text]) and dielectric loss ([Formula: see text]) over a broad frequency range. The dependence of AC conductivity and dielectric properties on the frequency and temperature for [Formula: see text] in pellet form obtained from [Formula: see text] single crystal were studied in the frequency range of (40 Hz-3 MHz) and temperature range of [Formula: see text]K. The AC conductivity of the [Formula: see text] was found to obey the power law, i.e., [Formula: see text]. AC conductivity of [Formula: see text] was dominated by the correlated barrier hopping (CBH) model. The obtained activation energy values of the AC conductivity have confirmed that the hopping conduction is the dominant one. A decrease in these values has noticed with the increase in frequency. The density of localized states [Formula: see text] close to Fermi level for [Formula: see text] was obtained in the range of [Formula: see text] cm[Formula: see text]) for various temperatures and frequency. The frequencies corresponding to maxima of the imaginary electric modulus at different temperatures were found to satisfy an Arrhenius law with activation energy [Formula: see text] of 0.32 eV. A decrease in the relaxation time τ was observed with the increase in temperature. The average hopping distance R and the average time of charge carrier hoping between localized states t were found in the range of 6.10-11.95 nm and [Formula: see text] s respectively, for the investigated range of frequency and the value of the binding energy [Formula: see text] was 0.52 eV. We report on the preparation, characterization, and analysis of [Formula: see text] semiconductor single crystals, focusing on the influence of frequency and temperature on AC conductivity. Utilizing X-ray diffraction, thermo gravimetric analysis, and dielectric property measurements, we delineate the material's structural and electrical properties. Complementing our experimental findings, Machine Learning (ML) models, including Random Forest and Gradient Boosting, were employed to predict AC conductivity, revealing significant predictors and corroborating the experimental insights with high accuracy. This interdisciplinary approach enhances our understanding of [Formula: see text]'s properties and demonstrates the potential of ML in materials science research.
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