Modifying the prediction model for conductivity in binary magnesium alloys: Incorporating the effect of second-phase conductivity

We proposed a model published in JMRT ( 2023 , 27 : 5924–5934 ) for designing and evaluating Mg alloys’ conductivity. However, subsequent research revealed that this model was inapplicable to Mg–Cu alloys, as the calculated values were significantly lower than the measured values due to the model’s neglect of the conductivity of second-phase in alloys. By studying the effect of the second phase’s conductivity in Mg-X (X = Cu, Sr, Al, Zn, Sn, and Ca) alloys, we concluded that the second phase’s conductivity significantly impacts the alloy’s overall conductivity, showing a positive correlation between the two. Based on these insights and experimental data from six binary Mg alloy systems, we propose a new-version model that incorporates the second phase’s conductivity. Compared to the initial model, the predictions of this new model were closer to the actual measured values across a broader range of experimental data. • The second phase’s conductivity cannot be ignored when studying the overall conductivity of alloys. • There is a positive correlation between the second phase’s conductivity and the overall conductivity of alloys. • Proposed a new predictive model of binary Mg alloys’ conductivity, which has better applicability compared to initial one.

Authors

Institutions

Publication Details

Journal
Journal of Materials Research and Technology
Published
2025-03-15
DOI
https://doi.org/10.1016/j.jmrt.2025.03.127
Citations
5
Primary Topic
Magnesium Alloys: Properties and Applications
Type
article
Field-Weighted Citation Impact
2.99

Funders

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

Modifying the prediction model for conductivity in binary magnesium alloys: Incorporating the effect of second-phase conductivity

Feng Luo, Zhaomeng Wang, Jie Chen, Hui Chen et al.
5 citations
Journal of Materials Research and Technology
Magnesium Alloys: Properties and Applications
2.99
article

Modifying the prediction model for conductivity in binary magnesium alloys: Incorporating the effect of second-phase conductivity

Feng Luo, Zhaomeng Wang, Jie Chen, Hui Chen, Yan Liu, Ying Zeng, Hongmei Liu, Bing Sun, Jie Ren
article en
5 citations

Abstract

We proposed a model published in JMRT ( 2023 , 27 : 5924–5934 ) for designing and evaluating Mg alloys’ conductivity. However, subsequent research revealed that this model was inapplicable to Mg–Cu alloys, as the calculated values were significantly lower than the measured values due to the model’s neglect of the conductivity of second-phase in alloys. By studying the effect of the second phase’s conductivity in Mg-X (X = Cu, Sr, Al, Zn, Sn, and Ca) alloys, we concluded that the second phase’s conductivity significantly impacts the alloy’s overall conductivity, showing a positive correlation between the two. Based on these insights and experimental data from six binary Mg alloy systems, we propose a new-version model that incorporates the second phase’s conductivity. Compared to the initial model, the predictions of this new model were closer to the actual measured values across a broader range of experimental data. • The second phase’s conductivity cannot be ignored when studying the overall conductivity of alloys. • There is a positive correlation between the second phase’s conductivity and the overall conductivity of alloys. • Proposed a new predictive model of binary Mg alloys’ conductivity, which has better applicability compared to initial one.

Journal of Materials Research and TechnologyVol. 36
Hefei Meiling (China) (CN), Materials Technologies (United States) (US), Southwest Jiaotong University (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 10%
Magnesium Alloys: Properties and Applications
2.99
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.