Evidence map›Paper›PMID 39597211›Full record

ArticleMicromachines2024

Replication of Leaf Surface Structures on Flat Phosphor-Converted LEDs for Enhanced Angular Color Uniformity.

Bing-Mau Chen, Chiu-Hsiang Chen, Shang-Ping Ying, Yu-Kang Chang

Abstract read
In one paragraph

Article in Micromachines, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Bing-Mau ChenDepartment of Semiconductor and Electro-Optical Technology, Minghsin University of Science & Technology, 1, Xinxing Road, Xinfeng, Hsin-Chu 30401, Taiwan.
Chiu-Hsiang ChenDepartment of Semiconductor and Electro-Optical Technology, Minghsin University of Science & Technology, 1, Xinxing Road, Xinfeng, Hsin-Chu 30401, Taiwan.
Shang-Ping YingDepartment of Semiconductor and Electro-Optical Technology, Minghsin University of Science & Technology, 1, Xinxing Road, Xinfeng, Hsin-Chu 30401, Taiwan.ORCID 0000-0002-5572-8651
Yu-Kang ChangDepartment of Medical Research, Tungs' Taichung MetroHarbor Hospital, Taichung City 43503, Taiwan.ORCID 0000-0002-2687-4801

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We explored the use of biomimetic structures, including those that mimic leaf structures, to enhance the angular color uniformity of flat phosphor-converted light-emitting diodes (pcLEDs). The distinct microstructures found on natural leaf surfaces, such as micro-scale bumps, ridges, and hierarchical patterns, have inspired the design of artificial microstructures that can improve light extraction, scattering, and overall optical performance in LED applications. The effects of these leaf surface microstructures on the phosphor layer of flat pcLEDs were evaluated. An imprinting technique was employed to directly replicate the surface morphology structures from fresh plant leaves. The results indicated that this method provided excellent scattering capability and reduced the disparity in light output between blue and yellow light emissions from flat pcLEDs at various angles. Subsequently, uniform correlated color temperature in the flat pcLEDs was achieved, reducing the yellow ring effect. Furthermore, the availability of diverse wrinkle and surface patterns from a wide range of natural prototypes could reduce design costs compared with traditional mold fabrication, making the method suitable for application in mass production.

Indexed as

angular color uniformityimprintingleaf surface microstructurephosphor-converted LEDs

Identifiers

PMID39597211
PMCPMC11596375

What OpenQuestion holds

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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.