Amorphous/Graphitic Carbon Phase Engineering of Corrosion-Resistant Fe@C Core-Shell Nanowires for Optimized Dipole Polarization and Enhanced Microwave Absorption
CHEMICAL ENGINEERING JOURNAL（2024）
Chongqing Univ Posts & Telecommun
Abstract
The study of designing and developing ferromagnetic nanocomposites with both microwave absorption (MA) and corrosion protection properties through microscopic heterogeneous interfacial engineering and crystal phase engineering remains challenging. In this study, Fe@C core-shell nanowires (FCNWs) are obtained by magnetic field-controlled in situ reduction method and annealing process to de-regulate the carbon shell thickness and carbon morphology. The enhancement of Fe-C heterogeneous relative interfacial effects and dielectric/magnetic response is revealed using density functional theory and simulated electromagnetic field distribution, and the modulation of the amorphous carbon/graphitic carbon ratio achieves the enhancement of dipole polarization in the low-frequency region. The FCNWs absorber had a minimum reflection loss (RLmin) of -47.6 dB at a thickness of 4.9 mm, and the maximum effective absorption bandwidth (EABmax) was achieved at 5.5 mm up to 4.0 GHz. In addition, the shielding effect of the graphitic carbon shell on the corrosion medium and the addition of additional electron conduction pathways allow the FCNWs absorber to obtain smaller corrosion currents and larger polarization resistances compared to Fe nanowires. Thus, modulation of atomic-scale heterogeneous interfaces and crystal phases has great potential in modulating the dielectric response.
MoreTranslated text
Key words
Heterogeneous interface engineering,Ferromagnetic nanowires,Microwave absorption,Corrosion protection,Carbon crystalline phases
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