Evidence map›Paper›PMID 39448653›Full record

ArticleScientific reports2024

Deep neural network-enabled dual-functional wideband absorbers.

Jing Li, BinYi Ma, Huanyang Chen, Rui Cai, SiMing Chen, Qiannan Wu, Mengwei Li

Abstract read
In one paragraph

Article in Scientific reports, 2024. 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

7 authors.

Jing LiSchool of Instrument and Electronics, North University of China, Taiyuan, 030051, China.
BinYi MaSchool of Instrument and Electronics, North University of China, Taiyuan, 030051, China.
Huanyang ChenDepartment of Physics, Xiamen University, Xiamen, 361000, China.
Rui CaiSchool of Instrument and Electronics, North University of China, Taiyuan, 030051, China.
SiMing ChenSchool of Instrument and Electronics, North University of China, Taiyuan, 030051, China.
Qiannan WuSchool of Instrument and intelligent future technology, North University of China, Taiyuan, 030051, China. qiannanwoo@nuc.edu.cn.
Mengwei LiSchool of Instrument and Electronics, North University of China, Taiyuan, 030051, China. lmwprew@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The increasing interest in switchable and tunable wideband perfect absorbers for applications such as modulation, energy harvesting, and spectroscopy has significantly driven research efforts. In this study, we present a dual-function terahertz (THz) metamaterial absorber supported by deep neural networks (DNN). This absorber achieves dual-wideband perfect absorption through the use of graphene and vanadium dioxide (VO₂), enabling both switching and tuning functionalities. Simulation results show that, in the insulating phase of VO₂, a high-frequency wideband absorption ranging from 9.31 to 9.77 THz is achieved, with an absorption rate exceeding 90%. In contrast, in the metallic phase of VO₂, a full-band wideband absorption above 90% is observed from 8.44 to 9.75 THz. The corresponding fractional bandwidths are 61.3% and 174.6%, respectively. Additionally, electrical tuning of graphene's Fermi level from 0.01 to 1 eV enables continuous modulation of absorption intensity between 48 and 100%. The absorber also exhibits polarization insensitivity to TE and TM waves due to its symmetric design and broad incidence angle. This design holds significant potential for various THz applications, including switching, electromagnetic shielding, stealth technology, filtering, and sensing.

Indexed as

Deep neural networks (DNN)Dual-functionDual-widebandMetamaterial absorber

Identifiers

PMID39448653
PMCPMC11502817

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.