Evidence map›Paper›PMID 40481002›Full record

ArticleNature communications2025

Optimizing structured surfaces for diffractive waveguides.

Yuntian Wang, Yuhang Li, Tianyi Gan, Kun Liao, Mona Jarrahi, Aydogan Ozcan

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Nature communications, 2025. 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. Review
  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

6 authors.

Yuntian Wang *Electrical and Computer Engineering Department, University of California, Los Angeles, CA, USA.ORCID http://orcid.org/0009-0003-1774-3636
Yuhang Li *Electrical and Computer Engineering Department, University of California, Los Angeles, CA, USA.ORCID http://orcid.org/0000-0002-9364-4125
Tianyi GanElectrical and Computer Engineering Department, University of California, Los Angeles, CA, USA.
Kun LiaoElectrical and Computer Engineering Department, University of California, Los Angeles, CA, USA.ORCID http://orcid.org/0000-0003-2872-6359
Mona JarrahiElectrical and Computer Engineering Department, University of California, Los Angeles, CA, USA.ORCID http://orcid.org/0000-0001-9514-555X
Aydogan OzcanElectrical and Computer Engineering Department, University of California, Los Angeles, CA, USA. ozcan@ucla.edu.ORCID http://orcid.org/0000-0002-0717-683X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We introduce universal diffractive waveguide designs that can match the performance of conventional dielectric waveguides and achieve various functionalities. Optimized using deep learning, diffractive waveguides can be cascaded to form any desired length and are comprised of transmissive diffractive surfaces that permit the propagation of desired modes with low loss and high mode purity. In addition to guiding the targeted modes through cascaded diffractive units, we also developed various waveguide components and introduced bent diffractive waveguides, rotating the direction of mode propagation, as well as spatial and spectral mode filtering and mode splitting diffractive waveguide designs, and mode-specific polarization control. This framework was experimentally validated in the terahertz spectrum to selectively pass certain spatial modes while rejecting others. Without the need for material dispersion engineering diffractive waveguides can be scaled to operate at different wavelengths, including visible and infrared spectrum, covering potential applications in, e.g., telecommunications, imaging, sensing and spectroscopy.

Identifiers

PMID40481002
PMCPMC12144198

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.