Synthesis of porous carbon embedded with NiCo/CoNiO2 hybrids composites for excellent electromagnetic wave absorption performance.

Two-dimensional nanomaterials for high-efficiency electromagnetic wave absorption: An overview of recent advances and prospects

Covalently bonded polyaniline/graphene composites as high-performance electromagnetic (EM) wave absorption materials Part A Applied science and manufacturing

Cd0.9Co0.1S nanostructures concentration study on the structural and optical properties of SWCNTs/PVA blend

A hierarchical micro flower structure NiCo2O4/GN/Fe3O4 composite was prepared with a two-step method. Then Fe3O4 nanoparticles were dressed up on petals of NiCo2O4/GN/Fe3O4 composite. It was successfully detected by instruments to analysis crystal texture, surface elements, micro morphology, magnetic properties and weight loss. Results showed that good synergistic effect of dielectric loss and magnetic loss of NiCo2O4/GN and Fe3O4 led to superior EM wave absorption. The minimum RL was − 38.18 dB at 10.6 GHz and effective absorption bandwidth < − 10 dB was reached 2.7 GHz (8.5–11.2 GHz) with the thickness 2.5 mm. The enhancement of EM wave absorption was attributed to the dielectric loss, magnetic loss, interfacial polarization, effective impedance matching and multiple scattering. Thus, the NiCo2O4/GN/Fe3O4 composite was a promising and developable material in EM wave absorption fields.

Synthesis of multilayered micro flower NiCo 2 O 4 /GN/Fe 3 O 4 composite for enhanced electromagnetic microwave (EM) absorption performance

Self-Assembly Magnetic FeCo Nanostructures on Oxide Graphene for Enhanced Microwave Absorption

In this paper, the Co/CoO composite was prepared via polyol reduction method, and the Co/CoO was wrapped by reduced graphene oxide (RGO) synthesized with Hummers’ method. The crystal texture, element composition, micromorphology, magnetic properties, and electromagnetic absorption performance of the composite were tested. Compared with the single RGO or Co/CoO, the electromagnetic absorption performance of the Co/CoO@RGO composite has been greatly improved. The RLmin (minimum reflection loss) is − 56.56 dB, the effective absorption bandwidth (RL ≤ − 10 dB) is 5.86 GHz when the ratio of the compound to the paraffin is 1:1, and the thickness is 1.9 mm. The absorption properties of the Co/CoO@RGO composite are excellent, partly due to the synergistic effect of the magnetic and dielectric materials, and partly due to various electromagnetic wave loss mechanisms inside the sample, including the interface polarization between the two materials, Debye relaxation of various substances. Thus, the Co/CoO@RGO composite is a promising material in the field of microwave absorption.

Synthesis of Co/CoO@RGO composite for enhanced electromagnetic microwave absorption performance

Intermetallic compounds are commonly used in semiconductor, shape memory materials, hydrogen storage materials and magnetic materials due to their unique physical and chemical properties. In this study, the FeNi3/rGO (rGO: reduced graphene oxide) composite was prepared by one-step hydrothermal method using water-soluble nickel and iron salts as metal sources and hydrazine hydrate as reducing agent in alkaline solution. The structure of the composites are characterized by XRD, FTIR, XPS and Raman spectroscopy. The micromorphology of the samples is observed by SEM and TEM. The electrochemical performance of the FeNi3/rGO composite is evaluated by electrochemical impedance spectroscopy (EIS), galvanostatic charge–discharge (GCD) and cyclic voltammetry (CV). The results indicate that when the scan rate is 10 mV s−1 in 4 M KOH electrolyte solution, the specific capacitance of the FeNi3/rGO composite is 169.42 F g−1. At a current density of 1 A g−1, the specific capacitance of the FeNi3/rGO composite remains 82.72% of the original energy after 3000 cycles. Therefore, the FeNi3/rGO composite has excellent capacitance performance and is expected to be applied in supercapacitors and nanodevices in the future.

One‐step hydrothermal synthesis of the FeNi3/rGO composite for electrochemical supercapacitor

Many studies have focused on performing variational-scale segmentation to represent various geographical objects in high-resolution remote-sensing images. However, it remains a significant challenge to select the most appropriate scales based on the geographical-distribution characteristics of ground objects. In this study, we propose a variational-scale multispectral remote-sensing image segmentation method using spectral indices. Real scenes in remote-sensing images contain different types of land cover with different scales. Therefore, it is difficult to segment images optimally based on the scales of different ground objects. To guarantee image segmentation of ground objects with their own scale information, spectral indices that can be used to enhance some types of land cover, such as green cover and water bodies, were introduced into marker generation for the watershed transformation. First, a vector field model was used to determine the gradient of a multispectral remote-sensing image, and a marker was generated from the gradient. Second, appropriate spectral indices were selected, and the kernel density estimation was used to generate spectral-index marker images based on the analysis of spectral indices. Third, a series of mathematical morphology operations were used to obtain a combined marker image from the gradient and the spectral index markers. Finally, the watershed transformation was used for image segmentation. In a segmentation experiment, an optimal threshold for the spectral-index-marker generation method was identified. Additionally, the influence of the scale parameter was analyzed in a segmentation experiment based on a five-subset dataset. The comparative results for the proposed method, the commonly used watershed segmentation method, and the multiresolution segmentation method demonstrate that the proposed method yielded multispectral remote-sensing images with much better performance than the other methods.

/pdf/variational-scale-segmentation-for-multispectral-remote-1jyf84tp.pdf

Variational-Scale Segmentation for Multispectral Remote-Sensing Images Using Spectral Indices

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Ligang Cheng

Author Tools

Chat about Author