Nanostructures for Infrared Imaging: Enhanced Detection

Advanced detection systems increasingly rely on infrared (IR) imaging to overcome the limitations of visible light cameras in adverse environments such as fog, rain, and low-light conditions. However, the effectiveness of IR detection remains fundamentally constrained by the low emissivity contrast between targets and their backgrounds. Here, we present a plasmonic metal-dielectric-metal nanostructure comprising a porous anodic aluminum oxide (AAO) dielectric layer sandwiched between an aluminum substrate and a surface Au nanoparticle layer that enables near-independent modulation of visible reflectance (400-800 nm) and long-wave infrared emissivity (8-14 μm). The decoupling mechanism exploits the distinct characteristic length scales governing each spectral band: visible reflectance is controlled by Fabry-Pérot cavity interference and plasmonic absorption of the Au nanoparticle layer, while infrared emissivity is governed by the intrinsic phonon absorption of the AAO layer and is insensitive to Au coverage. Using scalable anodic oxidation and screen-printing fabrication, we achieve tunable visible reflectance (R = 0.2-0.9) and infrared emissivity (ε = 0.1-0.87). Applied to infrared-enhanced license plate detection, our patterned plates achieve an average recognition rate of ∼45% under adverse environmental conditions, compared to ∼5% for conventional plates. This work offers a scalable route to multispectral patterned surfaces for infrared imaging, thermal sensing, and anticounterfeiting applications.

Authors

Institutions

Publication Details

Journal
ACS Applied Materials & Interfaces
Published
2026-05-31
DOI
https://doi.org/10.1021/acsami.6c03654
Primary Topic
Thermal Radiation and Cooling Technologies
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Nanostructures for Infrared Imaging: Enhanced Detection

D Zhang, Yuqin Xiong, Wei Zhang, Yitong Zhou et al.
ACS Applied Materials & Interfaces
Thermal Radiation and Cooling Technologies
article

Nanostructures for Infrared Imaging: Enhanced Detection

D Zhang, Yuqin Xiong, Wei Zhang, Yitong Zhou, Zhijian Zhang
article en

Abstract

Advanced detection systems increasingly rely on infrared (IR) imaging to overcome the limitations of visible light cameras in adverse environments such as fog, rain, and low-light conditions. However, the effectiveness of IR detection remains fundamentally constrained by the low emissivity contrast between targets and their backgrounds. Here, we present a plasmonic metal-dielectric-metal nanostructure comprising a porous anodic aluminum oxide (AAO) dielectric layer sandwiched between an aluminum substrate and a surface Au nanoparticle layer that enables near-independent modulation of visible reflectance (400-800 nm) and long-wave infrared emissivity (8-14 μm). The decoupling mechanism exploits the distinct characteristic length scales governing each spectral band: visible reflectance is controlled by Fabry-Pérot cavity interference and plasmonic absorption of the Au nanoparticle layer, while infrared emissivity is governed by the intrinsic phonon absorption of the AAO layer and is insensitive to Au coverage. Using scalable anodic oxidation and screen-printing fabrication, we achieve tunable visible reflectance (R = 0.2-0.9) and infrared emissivity (ε = 0.1-0.87). Applied to infrared-enhanced license plate detection, our patterned plates achieve an average recognition rate of ∼45% under adverse environmental conditions, compared to ∼5% for conventional plates. This work offers a scalable route to multispectral patterned surfaces for infrared imaging, thermal sensing, and anticounterfeiting applications.

ACS Applied Materials & Interfaces
Shanghai Jiao Tong University (CN), Metal Matrix Cast Composites (United States) (US), Leibniz-Institut für Verbundwerkstoffe GmbH (DE), CTI Engineering (Japan) (JP), State Key Laboratory of Metal Matrix Composites
Life in Land
Openalex Percentile: Top 9%
Thermal Radiation and Cooling Technologies
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.