Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 620

The DOI matches its Crossref record.

Infrared Plasmonic Refractive Index Sensor with Ultra-High Figure of Merit Based on the Optimized All-Metal Grating

Open access
Li, Ruifang, Wu, Dong, Liu, Yumin, Yu, Li, Yu, Zhongyuan, Ye, Han·2017·Nanoscale Research Letters

Abstract

A perfect ultra-narrow band infrared metamaterial absorber based on the all-metal-grating structure is proposed. The absorber presents a perfect absorption efficiency of over 98% with an ultra-narrow bandwidth of 0.66 nm at normal incidence. This high efficient absorption is contributed to the surface plasmon resonance. Moreover, the surface plasmon resonance-induced strong surface electric field enhancement is favorable for application in biosensing system. When operated as a plasmonic refractive index sensor, the ultra-narrow band absorber has a wavelength sensitivity 2400 nm/RIU and an ultra-high figure of merit 3640, which are much better than those of most reported similar plasmonic sensors. Besides, we also comprehensively investigate the influences of structural parameters on the sensing properties. Due to the simplicity of its geometry structure and its easiness to be fabricated, the proposed high figure of merit and sensitivity sensor indicates a competitive candidate for applications in sensing or detecting fields.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1186/s11671-016-1773-2

Cites

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cited by

See all 620

Metadata

Authors
Li, R., Wu, D., Liu, Y., Yu, L., Yu, Z., & Ye, H.
Venue
Nanoscale Research Letters
Published
2017-01-03
Volume
12
Issue
1
Pages
1-1
ISSN
  • 1556-276X (ISSN-L)
  • 1931-7573
OpenAlex
W2560989997
Cited by
620
References
0
FWCI
21.18
Type
journal article
Language
en
Open access
gold

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Infrared Plasmonic Refractive Index Sensor with Ultra-High Figure of Merit Based on the Optimized All-Metal Grating. https://app.cochat.ai/papers/10.1186/s11671-016-1773-2
The paper
Li, R., Wu, D., Liu, Y., Yu, L., Yu, Z., & Ye, H. (2017). Infrared Plasmonic Refractive Index Sensor with Ultra-High Figure of Merit Based on the Optimized All-Metal Grating. Nanoscale Research Letters. https://doi.org/10.1186/s11671-016-1773-2

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.