Effect of Diamagnetic Substitution on the Structure and Properties of Barium Hexaferrite
openalex(2024)
Abstract
Aluminum substituted BaFe12-xAlxO19, titanium substituted BaFe12-xTixO19, aluminum and titanium substituted BaFe12-x-yAlxTiyO19 (with x = y = 0.0, 0.1, 0.5, 1.0) barium hexaferrites have been synthesized using the solid phase method. The effect of Al+3 or/and Ti4+ substitution on crystal structure and properties of BaFe12-xAlxO19, BaFe12-yTiyO19 and BaFe12-x-yAlxTiyO19 was studied. It was found that the resulting samples consist of plate-like hexagonal particles with sizes in the range of 1-10 μm. The phase purity and the crystal structure of the obtained ferrite solid solutions have been studied by powder X-ray diffraction. The results of investigation the crystal structure confirmed the formation of a hexagonal single phase with space group P63/mmc (194) in the synthesized samples. In all samples smaller ionic radii aluminum decreased cell parameters. On the other hand, aliovalent Ti4+substation due to the possibility of Fe2+ formation led decreased cell parameters. Magnetization for all the samples is saturated at room temperature in fields of ~ 3 T. The magnetization decreases from for all samples. The coactivity decreased for BaFe12-xAlxO19 and decreased for BaFe12-x-yAlxTiyO19 more likely due to changing the crystalline particle sizes. The Curie temperature decreased, which was due to a change in the magnetic structure of the resulting ferrite-based solid solutions.
MoreTranslated text
Key words
Martensite Transformation
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined