Browse Articles

The influence of frequency and temperature on the AC-conductivity in $$\text {TlInTe}_2$$ semiconductor single crystal

Abstract A special design, based on the Bridgman technique, was used in our laboratory for preparing single crystals of $$\text {TlInTe}_2$$ . The structure of $$\text {TlInTe}_2$$ in powder form was examined using X-ray diffraction. $$\text {TlInTe}_2$$ at room temperature was found to be a tetragonal system with lattice parameters of $$a = 8.494$$ Å and $$c = 7.181$$ Å. The structural parameters, such as crystallite size D, micro strain $$\epsilon$$ , dislocation density $$\delta$$ , and unit cell parameters were determined from XRD spectra. Thermo gravimetric analysis (TGA) was employed to study the thermal behavior of $$\text {TlInTe}_2$$ , showcasing its significance in solid state physics. The TGA curve of $$\text {TlInTe}_2$$ exhibited distinct weight loss events corresponding to thermal decomposition processes. The frequency and temperature dependence of Ac-conductivity in a $$\text {TlInTe}_2$$ single crystal was studied by assessing the permittivity ( $$\epsilon _r$$ ) and dielectric loss ( $$\tan \delta$$ ) over a broad frequency range. The dependence of AC conductivity and dielectric properties on the frequency and temperature for $$\text {TlInTe}_2$$ in pellet form obtained from $$\text {TlInTe}_2$$ single crystal were studied in the frequency range of (40 Hz–3 MHz) and temperature range of $$(290{-}395)^{\circ }$$ K. The AC conductivity of the $$\text {TlInTe}_2$$ was found to obey the power law, i.e., $$\sigma _{ac} (\omega ) = A \omega ^s$$ . AC conductivity of $$\text {TlInTe}_2$$ 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 $$N (E_F)$$ close to Fermi level for $$\text {TlInTe}_2$$ was obtained in the range of $$(1.02{-}2.8 \times 10^{19}\ \text {eV}^{-1}$$ cm $$^{-3}$$ ) 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 $$E_R$$ of 0.32 eV. A decrease in the relaxation time $$\tau$$ 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 $$2 \times 10^{-7} {-} 2.4 \times 10^{-2}$$ s respectively, for the investigated range of frequency and the value of the binding energy $$W_m$$ was 0.52 eV. We report on the preparation, characterization, and analysis of $$\text {TlInTe}_2$$ 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 $$\text {TlInTe}_2$$ ’s properties and demonstrates the potential of ML in materials science research.

Chiral Locomotion Transitions of an Active Gel and Their Chemomechanical Origin

The feasibility of point-of-care testing for initial urinary liver fatty acid-binding protein to estimate severity in severe heatstroke

Modulation of Protein–Protein Interactions with Molecular Glues in a Synthetic Condensate Platform

MVB fault diagnosis based on time-frequency analysis and convolutional neural networks

A questionnaire survey of cervical and breast cancer screening among female employees and employees’ spouses

Sea turtles use magnetic ‘map’ and ‘compass’ to navigate the ocean

Real-world data analysis for factors influencing the quality check status in FoundationOne CDx cancer genomic profiling tests

Spatial Scale Matters: Hydrolysis of Aryl Methyl Ethers over Zeolites

An analytical solution for dynamic instability and vibration analysis of structural members with open and closed sections

The association between the follicular distribution pattern of polycystic ovaries and metabolic syndrome development in patients with polycystic ovary syndrome a prospective cohort study

Sequential and Time-Controlled Sol–Gel Transitions by Mechanical Switching of Molecular Tweezers

Energy efficiency optimization of electric hydraulic loader with variable speed variable displacement power source

I work remotely — on Mars

Reticular Synthesis of Covalent Organic Frameworks with kgd-v Topology and Trirhombic Pores

Postmortal epithelial changes of donor corneas impair applicability of a refractive ultraviolet femtosecond laser

Abstract This study evaluates the corneal applicability of a refractive ultraviolet femtosecond laser in postmortal human donor eyes and ex vivo porcine eyes. Refractive lenticule extraction and flap creation were attempted in 10 human donor eyes and 80 ex vivo porcine eyes with and without abrasion of the corneal epithelium. The postmortem interval ranged from 6 to 35 h in the human samples and was set to 4, 24, and 48 h for the porcine specimens. Nine human eyes and 60 porcine eyes were treated with an ultraviolet femtosecond laser. The rest was treated with an infrared laser. Optical coherence tomography and scanning electron microscopy were used to demonstrate success or failure of the procedures. Ultraviolet laser-assisted refractive surgery attempts without prior abrasion of the corneal epithelium were only successful at 6 h p.m. in the human eyes and at 4 and 24 h in the porcine eyes. Upon epithelial abrasion, refractive surgery was always successful with the ultraviolet laser. The infrared laser always performed successfully with and without prior epithelial abrasion. Thus, postmortal changes in the corneal epithelium impair the ability of refractive ultraviolet femtosecond lasers to create stromal cuts. This progresses with time but does not affect infrared femtosecond lasers.

Author Correction: Non-targeted LC–MS metabolomics reveals serum metabolites for high-altitude adaptation in Tibetan donkeys

Cu^δ+ Site-Enhanced Adsorption and Crown Ether-Reconfigured Interfacial D₂O Promote Electrocatalytic Dehalogenative Deuteration

Optimization design of cross border intelligent marketing management model based on multi layer perceptron-grey wolf optimization convolutional neural network

Abstract The cross-border intelligent marketing algorithm based on traditional linear models is relatively single in information feature extraction, making it difficult to effectively handle complex scenarios containing a large amount of implicit information in users and markets, resulting in poor personalized marketing effectiveness. To address this issue, this article proposes a cross-border intelligent marketing model that integrates rating information and user labels using a multi-layer perceptron grey wolf optimization and convolutional neural network (MLP-GWO-CNN). This model extracts implicit high-order information through nonlinear methods and can handle complex and sparse marketing data. Firstly, a dual path deep network structure was designed, in which one path was modeled using a multi-layer perceptron (MLP) to extract user interest features based on historical interaction ratings; Another path utilizes Convolutional Neural Networks (CNN) to extract semantic features from user label information and construct item feature representations. In response to the sensitivity of MLP algorithm to initial values and its tendency to fall into local optima, this paper uses GWO algorithm to optimize MLP. Next, the latent feature vectors generated by MLP and CNN are fused in the output layer to generate the final predictive marketing strategy last. Experiments were conducted using a real cross-border e-commerce dataset, and the results showed that compared with traditional recommendation algorithms, the MLP-GWO-CNN model proposed in this paper performs better in utilizing user tag information, effectively improving the accuracy and personalization of marketing recommendations. The accuracy of the model is over 89%, and the recall rate is over 90%.

Systemic inflammation index is a predictive and prognostic factor in patients with liposarcoma or leiomyosarcoma treated with trabectedin