Evidence map›Paper›PMID 38893768›Full record

ArticleMaterials (Basel, Switzerland)2024

Theoretical Prediction of Strengthening in Nanocrystalline Cu with Multi-Element Grain Boundary Segregation Decoration.

Fuli Guo, Chuanying Li, Tao Fu, Xianghe Peng

Abstract read
In one paragraph

Article in Materials (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

4 authors.

Fuli GuoDepartment of Engineering Mechanics, Chongqing University, Chongqing 400044, China.ORCID 0000-0001-9567-5454
Chuanying LiDepartment of Engineering Mechanics, Chongqing University, Chongqing 400044, China.ORCID 0000-0002-5671-8017
Tao FuDepartment of Engineering Mechanics, Chongqing University, Chongqing 400044, China.ORCID 0000-0001-5488-6511
Xianghe PengDepartment of Engineering Mechanics, Chongqing University, Chongqing 400044, China.

Funding

Chongqing Natural Science Foundation CSTB2022NSCQ-BHX0704Chongqing Natural Science Foundation CSTB2022NSCQ-MSX0383National Natural Science Foundation of China 11932004National Natural Science Foundation of China 12272066National Natural Science Foundation of China 12302130
6 · The paper itself

Abstract

The composition of grain boundaries (GBs) determines their mechanical behavior, which in turn affects the mechanical properties of nanocrystalline materials. Inspired by GB segregation and the concept of high-entropy alloys (HEAs), we investigated, respectively, the mechanical responses of nanocrystalline Cu samples with and without multi-element GBs, as well as the grain size effects, aiming to explore the effects of GB composition decoration on mechanical properties. Our results show that introducing multi-element segregation GBs can significantly improve the mechanical properties of nanocrystalline Cu by effectively inhibiting GB migration and sliding. Additionally, we proposed an improved a theoretical model that can reasonably describe the strengths of the materials with multi-element or single-element segregation GBs. Notably, the introduction of multi-element segregation GBs inhibits both migration and sliding behavior, with migration being more effectively suppressed than sliding. These results present a novel approach for designing high-performance nanometallic materials and offer valuable insights into the role of GB composition decoration in enhancing mechanical properties.

Indexed as

grain boundary strengtheninghigh-entropy alloymechanical propertiesmicrostructure

Identifiers

PMID38893768
PMCPMC11172879

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.