In the present study, combustion technique is adopted to study the impact of Mg ion doping on ZnAI₂O₄ nanoparticles (NPs). L-arginine is used as a fuel component. The Mg ions play a pivotal role in persuading various characteristics of ZnAI₂O₄ NPs. Various characterization technqiues such as Fourier transform infrared spectroscopy (FT-IR), X-ray diffraction (XRD), energy dispersive X-ray analysis (EDX), high resolution scanning electron microscopy (HR-SEM), diffuse reflectance spectroscopy (DRS), Thermo-gravimetric/differential thermal analysis (TG-DTA) and vibrating sample magnetometer (VSM) were carried out in order to synthesize the nanoparticles. Single phase cubic spinel structure of ZnAl₂O₄ (gahnite) formation was confirmed from the XRD characterization process of the nanoparticles. Estimated average crystallite size range of 11.85 nm to 19.02 nm was observed from Debye-Scherrer. Spherical morphology with uniform distributions was observed from HR-SEM characterization images. From the band gap studies, the attained band gap values were found to lie within 5.41 eV-4.66 eV range. The ZnAl₂O₄ and Mg:ZnAl₂O₄ NPs exhibited super-paramagnetic nature confirmed by magnetic measurements. The obtained results make ZnAl₂O ₄and Mg:ZnAl₂O₄ NPs appropriate for various optical, catalytic, energy and data storage applications.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1166/jnn.2021.19478 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
High-throughput evaluation of half-metallicity of CoMnSi Heusler alloys using composition-spread films and spin-integrated hard X-ray photoelectron spectroscopy.
Sci Technol Adv Mater
January 2025
Magnetic Functional Device Group, Research Center for Magnetic and Spintronic Materials (CMSM), National Institute for Materials Science (NIMS), Tsukuba, Japan.
We demonstrate high-throughput evaluation of the half-metallicity of CoMnSi Heusler alloys by spin-integrated hard X-ray photoelectron spectroscopy (HAXPES) of composition-spread films performed with high-brilliance synchrotron radiation at NanoTerasu, which identifies the optimum composition showing the best half-metallicity. Co Mn Si composition-spread thin films for = 10-40% with a thickness of 30 nm are fabricated on MgO(100) substrates using combinatorial sputtering technique. The 2-ordering and (001)-oriented epitaxial growth of CoMnSi are confirmed by X-ray diffraction for = 18-40%.
View Article and Find Full Text PDFStrategic Integration of Machine Learning in the Design of Excellent Hybrid Perovskite Solar Cells.
J Phys Chem Lett
January 2025
College of Chemistry and Materials Science, Hebei University, Baoding 071002, P. R. China.
The photoelectric conversion efficiency (PCE) of perovskites remains beneath the Shockley-Queisser limit, despite its significant potential for solar cell applications. The present focus is on investigating potential multicomponent perovskite candidates, particularly on the application of machine learning to expedite band gap screening. To efficiently identify high-performance perovskites, we utilized a data set of 1346 hybrid organic-inorganic perovskites and employed 11 machine learning models, including decision trees, convolutional neural networks (CNNs), and graph neural networks (GNNs).
View Article and Find Full Text PDFFirst-Principles Assessment of ZnTe and CdSe as Prospective Tunnel Barriers at the InAs/Al Interface.
ACS Appl Mater Interfaces
January 2025
Department of Materials Science and Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States.
Majorana zero modes are predicted to emerge in semiconductor/superconductor interfaces, such as InAs/Al. Majorana modes could be utilized for fault tolerant topological qubits. However, their realization is hindered by materials challenges.
View Article and Find Full Text PDFPhase transitions, Dirac and Weyl semimetal states in MnGeBiTe.
Sci Rep
January 2025
Saint Petersburg State University, St. Petersburg, 198504, Russia.
Using angle-resolved photoemission spectroscopy (ARPES) and density functional theory (DFT), an experimental and theoretical study of changes in the electronic structure (dispersion dependencies) and corresponding modification of the energy band gap at the Dirac point (DP) for topological insulator (TI) [Formula: see text] have been carried out with gradual replacement of magnetic Mn atoms by non-magnetic Ge atoms when concentration of the latter was varied from 10% to 75%. It was shown that when Ge concentration increases, the bulk band gap decreases and reaches zero plateau in the concentration range of 45-60% while trivial surface states (TrSS) are present and exhibit an energy splitting of 100 and 70 meV in different types of measurements. It was also shown that TSS disappear from the measured band dispersions at a Ge concentration of about 40%.
View Article and Find Full Text PDFEOSnet: Embedded Overlap Structures for Graph Neural Networks in Predicting Material Properties.
J Phys Chem Lett
January 2025
Department of Physics, Rutgers University, Newark, New Jersey 07102, United States of America.
Graph Neural Networks (GNNs) have emerged as powerful tools for predicting material properties, yet they often struggle to capture many-body interactions and require extensive manual feature engineering. Here, we present EOSnet (Embedded Overlap Structures for Graph Neural Networks), a novel approach that addresses these limitations by incorporating Gaussian Overlap Matrix (GOM) fingerprints as node features within the GNN architecture. Unlike models that rely on explicit angular terms or human-engineered features, EOSnet efficiently encodes many-body interactions through orbital overlap matrices, providing a rotationally invariant and transferable representation of atomic environments.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!