Evidence map›Paper›PMID 42017433›Full record

ArticleACS applied materials & interfaces2026

Grain Size-Dependent Defect and Domain Evolution in Lead Titanate-Based Relaxor Ferroelectrics.

Hangfeng Zhang, Yichen Wang, Zilong Li, Soyoung Oh, Junjie Liu, Haixue Yan, Yang Hao, Lei Su

Abstract read
In one paragraph

Article in ACS applied materials & interfaces, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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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

8 authors.

Hangfeng ZhangSchool of Engineering and Material Science, Queen Mary University of London, Mile End Road, London E1 4NS, U.K.ORCID 0000-0002-3928-8772
Yichen WangState Key Laboratory of Powder Metallurgy, Central South University, Changsha 410083, China.
Zilong LiSchool of Engineering and Material Science, Queen Mary University of London, Mile End Road, London E1 4NS, U.K.
Soyoung OhDepartment of Physics, University of Oxford, Oxford OX1 3PU, U.K.
Junjie LiuSchool of Physical and Chemical Science, Queen Mary University of London, Mile End Road, London E1 4NS, U.K.
Haixue YanSchool of Engineering and Material Science, Queen Mary University of London, Mile End Road, London E1 4NS, U.K.ORCID 0000-0002-4563-1100
Yang HaoSchool of Electronic Engineering and Computer Science, Queen Mary University of London, Mile End Road, London E1 4NS, U.K.ORCID 0000-0002-9949-7226
Lei SuSchool of Engineering and Material Science, Queen Mary University of London, Mile End Road, London E1 4NS, U.K.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Ferroelectric materials are widely used in diverse applications, where their performance is strongly dominated by grain size. Here, dense Er-doped lead titanate-based relaxor ferroelectrics were synthesized via spark plasma sintering, enabling precise grain size control from 0.9 to 11.1 μm. Fine-grained ceramics exhibit high defect concentrations and internal stress, stabilizing the tetragonal phase and resulting in weak, disordered polarization with low domain wall density and constrained mobility. At intermediate grain sizes, dense nanodomain networks with narrow walls (∼150 nm) form, allowing sharp and reversible polarization switching. Coarse-grained ceramics develop hierarchical, web-like domains with thicker walls (∼400 nm), reducing the pinning effect and enhancing wall mobility. Both saturation and remanent polarizations increase with grain size up to 5.4 μm before plateauing, while the piezoelectric coefficient rises by 200%, reaching 723 pC N

Indexed as

domain engineeringgrain size controlpiezoelectricrelaxor ferroelectricsspark plasma sintering

Identifiers

PMID42017433
PMCPMC13154116

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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.