Evidence map›Paper›PMID 33830726›Full record

ArticleACS nano2021

Large Area Patterning of Nanoparticles and Nanostructures: Current Status and Future Prospects.

Hannah-Noa Barad, Hyunah Kwon, Mariana Alarcón-Correa, Peer Fischer

Abstract read
In one paragraph

Article in ACS nano, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing 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

19 citing papers in PubMed.

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  16. PhotocleavableChemical reviews · 2023
    Review
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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

4 authors.

Hannah-Noa BaradMax Planck Institute for Intelligent Systems, Heisenbergstrasse 3, 70569 Stuttgart, Germany.ORCID http://orcid.org/0000-0003-0764-6421
Hyunah KwonMax Planck Institute for Intelligent Systems, Heisenbergstrasse 3, 70569 Stuttgart, Germany.ORCID http://orcid.org/0000-0002-5300-1002
Mariana Alarcón-CorreaMax Planck Institute for Intelligent Systems, Heisenbergstrasse 3, 70569 Stuttgart, Germany.ORCID http://orcid.org/0000-0002-7165-1906
Peer FischerMax Planck Institute for Intelligent Systems, Heisenbergstrasse 3, 70569 Stuttgart, Germany.ORCID http://orcid.org/0000-0002-8600-5958

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Nanoparticles possess exceptional optical, magnetic, electrical, and chemical properties. Several applications, ranging from surfaces for optical displays and electronic devices, to energy conversion, require large-area patterns of nanoparticles. Often, it is crucial to maintain a defined arrangement and spacing between nanoparticles to obtain a consistent and uniform surface response. In the majority of the established patterning methods, the pattern is written and formed, which is slow and not scalable. Some parallel techniques, forming all points of the pattern simultaneously, have therefore emerged. These methods can be used to quickly assemble nanoparticles and nanostructures on large-area substrates into well-ordered patterns. Here, we review these parallel methods, the materials that have been processed by them, and the types of particles that can be used with each method. We also emphasize the maximal substrate areas that each method can pattern and the distances between particles. Finally, we point out the advantages and disadvantages of each method, as well as the challenges that still need to be addressed to enable facile, on-demand large-area nanopatterning.

Indexed as

chemical/physical patterninglarge-area patterningmagnetic/electric field patterningnanoparticle patternsnonlithographic methodsnontemplate nanopatternsorganic templatesparallel methodstemplate nanopatterns

Identifiers

PMID33830726
PMCPMC8155328

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.