Enhancing Magneto‐Dielectric Material Characterization by Integrating SRR Sensor, De‐Embedding Procedure, and Artificial Neural Network Modeling

Abstract This work presents an improved methodology for characterizing the effective permittivity ( ε ) and permeability ( μ ) of magnetodielectric (MD) composites, using Fe 3 O 4 nanoparticles dispersed in a PDMS polymer matrix. The proposed approach integrates a planar split‐ring resonator sensor design and an artificial neural network model for parameter extraction, complemented by a line‐line deembedding methodology to eliminate parasitic effects from measurements. The experimental results are compared with predictions derived from the Maxwell‐Garnett and Polder‐Van Santen effective medium models, demonstrating a close agreement between theory and measurements. This study highlights the importance of accurate experimental procedures and advanced modeling techniques in understanding the electromagnetic behavior of MD composites and offers insights into their potential for developing next‐generation microwave components.

Authors

Institutions

Publication Details

Journal
Radio Science
Published
2025-10-01
DOI
https://doi.org/10.1029/2024rs008214
Primary Topic
Microwave and Dielectric Measurement Techniques
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Enhancing Magneto‐Dielectric Material Characterization by Integrating SRR Sensor, De‐Embedding Procedure, and Artificial Neural Network Modeling

Alejandro Pons-Abenza, Kezhen Hui, Humberto Lobato‐Morales, Camilo Vélez et al.
Radio Science
Microwave and Dielectric Measurement Techniques
article

Enhancing Magneto‐Dielectric Material Characterization by Integrating SRR Sensor, De‐Embedding Procedure, and Artificial Neural Network Modeling

Alejandro Pons-Abenza, Kezhen Hui, Humberto Lobato‐Morales, Camilo Vélez, M. A. G. Laso, Gabriela Méndez‐Jerónimo, Iván Arregui, T. Lopetegi, Naji Tarabay, Germán A. Álvarez-Botero, Nathalia Duque-Madrid
article en

Abstract

Abstract This work presents an improved methodology for characterizing the effective permittivity ( ε ) and permeability ( μ ) of magnetodielectric (MD) composites, using Fe 3 O 4 nanoparticles dispersed in a PDMS polymer matrix. The proposed approach integrates a planar split‐ring resonator sensor design and an artificial neural network model for parameter extraction, complemented by a line‐line deembedding methodology to eliminate parasitic effects from measurements. The experimental results are compared with predictions derived from the Maxwell‐Garnett and Polder‐Van Santen effective medium models, demonstrating a close agreement between theory and measurements. This study highlights the importance of accurate experimental procedures and advanced modeling techniques in understanding the electromagnetic behavior of MD composites and offers insights into their potential for developing next‐generation microwave components.

Radio ScienceVol. 60(10)
Universidad Pública de Navarra (UPNA) (ES), Micro Magnetics (United States) (US), Centro de Investigación Científica y de Educación Superior de Ensenada (MX)
Ministerio de Ciencia e Innovación, Universidad Pública de Navarra, University of California, Irvine, Agencia Estatal de Investigación
Openalex Percentile: Top 72%
Microwave and Dielectric Measurement Techniques
DOIOpen Access PDFOpenAlex
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.