Evidence map›Paper›PMID 38108267›Full record

ArticleACS nano2023

Topological Learning for the Classification of Disorder: An Application to the Design of Metasurfaces.

Tristan Madeleine, Nina Podoliak, Oleksandr Buchnev, Ingrid Membrillo Solis, Tetiana Orlova, Maria van Rossem, Malgosia Kaczmarek, Giampaolo D'Alessandro, Jacek Brodzki

Abstract read
In one paragraph

Article in ACS nano, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

9 authors.

Tristan MadeleineMathematical Sciences, University of Southampton, Southampton SO17 1BJ, United Kingdom.ORCID 0000-0001-7655-8367
Nina PodoliakPhysics and Astronomy, University of Southampton, Southampton SO17 1BJ, United Kingdom.
Oleksandr BuchnevOptoelectronics Research Centre and Centre for Photonic Metamaterials, University of Southampton, Southampton SO17 1BJ, United Kingdom.ORCID 0000-0001-6161-2797
Ingrid Membrillo SolisMathematical Sciences, University of Southampton, Southampton SO17 1BJ, United Kingdom.
Tetiana OrlovaPhysics and Astronomy, University of Southampton, Southampton SO17 1BJ, United Kingdom.ORCID 0000-0002-1594-291X
Maria van RossemPhysics and Astronomy, University of Southampton, Southampton SO17 1BJ, United Kingdom.
Malgosia KaczmarekPhysics and Astronomy, University of Southampton, Southampton SO17 1BJ, United Kingdom.
Giampaolo D'AlessandroMathematical Sciences, University of Southampton, Southampton SO17 1BJ, United Kingdom.
Jacek BrodzkiMathematical Sciences, University of Southampton, Southampton SO17 1BJ, United Kingdom.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Structural disorder can improve the optical properties of metasurfaces, whether it is emerging from some large-scale fabrication methods or explicitly designed and built lithographically. For example, correlated disorder, induced by a minimum inter-nanostructure distance or by hyperuniformity properties, is particularly beneficial for light extraction. Inspired by topology, we introduce numerical descriptors to provide quantitative measures of disorder with universal properties, suitable to treat both uncorrelated and correlated disorder at all length scales. The accuracy of these topological descriptors is illustrated both theoretically and experimentally by using them to design plasmonic metasurfaces with controlled disorder that we then correlate to the strength of their surface lattice resonances. These descriptors are an example of topological tools that can be used for the fast and accurate design of disordered structures or as aid in improving their fabrication methods.

Indexed as

designdisordermetasurfaceoptimizationplasmonicsurface lattice resonancetopological data analysis

Identifiers

PMID38108267
PMCPMC10796169

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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.