Evidence map›Paper›PMID 40004276›Full record

ArticleMaterials (Basel, Switzerland)2025

Revisiting the Dependence of Electrical Resistivity on Cu-Rich Precipitates in an Aged Fe-Cu Model Alloy: A Microstructure-Based Prediction Model.

Shengjun Xia, Menglin Gao, Xing Hu, Chunfa Huang, Shuaiheng Liang, Wenlu Zhang, Qiulin Li

Abstract read
In one paragraph

Article in Materials (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Shengjun XiaShenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China.ORCID 0009-0002-3519-7213
Menglin GaoShenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China.
Xing HuShenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China.
Chunfa HuangShenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China.
Shuaiheng LiangShenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China.
Wenlu ZhangShenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China.
Qiulin LiShenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China.ORCID 0000-0001-6871-514X

Funding

National Key Research and Development Program of China 2022YFB3707200Shenzhen Science and Technology Innovation Program WDZC20200821100123001
6 · The paper itself

Abstract

Nanoscale Cu-rich precipitates (CRPs) play a crucial role in the irradiation embrittlement of reactor pressure vessels (RPVs), and binary Fe-Cu alloys serve as practical models to study the evolution of these precipitates. This study investigates the electrical resistivity of an Fe-1.17 wt.% Cu model alloy aged at 450 °C to enhance the understanding of electrical measurements for the non-destructive assessment of RPV irradiation embrittlement. Multi-level characterization methods were used to obtain quantitative data on multi-scale microstructures, including precipitates, dislocations, and grains. The formation and growth of CRPs were found to align closely with the Johnson-Mehl-Avrami model, and the variation in electrical resistivity showed a strong correlation with the evolution of the microstructure. Combined with detailed quantitative microstructure evolution analysis, an electrical resistivity prediction model that considers microstructural mechanisms has been developed. This model can accurately show the effect of CRPs on resistivity and can potentially be extended to RPV steels with other solute-rich precipitates, with a maximum absolute percentage error not exceeding 5%. These results provide a robust basis for the non-destructive and in-service evaluation of RPV irradiation embrittlement using electrical resistivity.

Indexed as

Cu-rich precipitateselectrical resistivitynon-destructive evaluationprediction modelRPV embrittlement

Identifiers

PMID40004276
PMCPMC11857174

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.