Evidence map›Paper›PMID 34106715›Full record

ArticleNano letters2021

Atomic-Level Structural Engineering of Graphene on a Mesoscopic Scale.

Alberto Trentino, Jacob Madsen, Andreas Mittelberger, Clemens Mangler, Toma Susi, Kimmo Mustonen, Jani Kotakoski

Abstract read
In one paragraph

Article in Nano letters, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing 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

14 citing papers in PubMed.

  1. Article
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  3. One-Size-Fits-All: A Universal Binding Site for Single-Layer Metal Cluster Self-Assembly.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025
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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.

Alberto TrentinoUniversity of Vienna, Faculty of Physics, Boltzmanngasse 5, 1090 Vienna, Austria.
Jacob MadsenUniversity of Vienna, Faculty of Physics, Boltzmanngasse 5, 1090 Vienna, Austria.
Andreas MittelbergerNion Company, 11511 NE 118th Street, Kirkland, Washington 98034, United States.ORCID 0000-0001-9267-5184
Clemens ManglerUniversity of Vienna, Faculty of Physics, Boltzmanngasse 5, 1090 Vienna, Austria.
Toma SusiUniversity of Vienna, Faculty of Physics, Boltzmanngasse 5, 1090 Vienna, Austria.ORCID 0000-0003-2513-573X
Kimmo MustonenUniversity of Vienna, Faculty of Physics, Boltzmanngasse 5, 1090 Vienna, Austria.ORCID 0000-0002-0953-7299
Jani KotakoskiUniversity of Vienna, Faculty of Physics, Boltzmanngasse 5, 1090 Vienna, Austria.ORCID 0000-0002-1301-5266

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Structural engineering is the first step toward changing properties of materials. While this can be at relative ease done for bulk materials, for example, using ion irradiation, similar engineering of 2D materials and other low-dimensional structures remains a challenge. The difficulties range from the preparation of clean and uniform samples to the sensitivity of these structures to the overwhelming task of sample-wide characterization of the subjected modifications at the atomic scale. Here, we overcome these issues using a near ultrahigh vacuum system comprised of an aberration-corrected scanning transmission electron microscope and setups for sample cleaning and manipulation, which are combined with automated atomic-resolution imaging of large sample areas and a convolutional neural network approach for image analysis. This allows us to create and fully characterize atomically clean free-standing graphene with a controlled defect distribution, thus providing the important first step toward atomically tailored two-dimensional materials.

Indexed as

GraphiteVacuumGraphiteautomationdefect engineeringelectron microscopygraphenemachine learning

Identifiers

PMID34106715
PMCPMC8227467

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.