Evidence map›Paper›PMID 42500736›Full record

ArticleNational science review2026

A general data-driven framework for scalable electron tomography.

Han Li, Wenting Cui, Huan Lei, Ziqi Chen, Yinpin Wei, Xuan Luo, Jiali Yang, Kuang Yu, Jia Li, Lin Gan

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in National science review, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

10 authors.

Han LiInstitute of Materials Research, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China.
Wenting CuiInstitute of Materials Research, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China.
Huan LeiInstitute of Materials Research, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China.
Ziqi ChenInstitute of Materials Research, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China.
Yinpin WeiInstitute of Materials Research, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China.
Xuan LuoInstitute of Materials Research, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China.
Jiali YangInstitute of Materials Research, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China.
Kuang YuInstitute of Materials Research, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China.
Jia LiInstitute of Materials Research, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China.
Lin GanInstitute of Materials Research, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Electron tomography (ET) is crucial for determining the three-dimensional (3D) structure of materials in real space but challenging due to the inherent missing wedge, high dose, and limited depth-of-field. Although deep learning can address these challenges in principle, the scarcity of ET data severely limits its application. In this study, we propose a general data-driven ET reconstruction framework that uses extensive and readily available random high-entropy projections to construct large-scale datasets. By integrating both real structural priors and depth-dependent imaging physics, the framework enables high-quality 3D reconstruction independent of specific materials or resolutions. Using this strategy, we successfully determine the 3D atomic structure of a 13-nm Pt nanoparticle containing 52 138 atoms, achieving a root-mean-square displacement of 22.6 pm; the projection consistency error is significantly reduced, effectively expanding the depth-of-field limit of atomic-scale ET.

Indexed as

atomic electron tomographydeep learningdepth-of-fieldmulti-scale 3D imaging

Identifiers

PMID42500736
PMCPMC13397574

What OpenQuestion holds

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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.