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NIR-Reflective Black Photonic Films Designed for Effective LiDAR Recognition
Hyunho Choi1, Sihun Park1, Chunghwan Jung2
1Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea.
ACS Applied Materials & Interfaces
|January 17, 2025
Summary
Researchers developed novel black photonic films that reflect near-infrared (NIR) light for improved autonomous driving LiDAR systems. These durable, eco-friendly coatings enhance LiDAR recognition while maintaining dark aesthetics.
Area of Science:
- Materials Science
- Optics
- Nanotechnology
Background:
- Conventional dark paints absorb visible and near-infrared (NIR) light, hindering LiDAR recognition in autonomous vehicles.
- Developing materials that selectively reflect NIR while appearing dark is crucial for advanced driver-assistance systems.
Purpose of the Study:
- To propose and evaluate colloidal photonic crystals as a novel solution for LiDAR-assistive coatings.
- To achieve a balance between low visible light absorption and high NIR reflectivity for autonomous driving applications.
Main Methods:
- Fabrication of colloidal photonic crystals via spontaneous self-assembly of silica particles in photocurable resin.
- Tuning the NIR stopband by controlling particle volume fraction.
- Incorporation of carbon black nanoparticles into interstitial regions to render films black and optimize optical properties.
Main Results:
- The developed black photonic films exhibit a tunable NIR stopband.
- Optimized carbon black concentration achieved low visible light transmission and high LiDAR intensity.
- Photonic films demonstrated superior LiDAR intensity compared to commercial dark paints and matched perylene black coatings.
Conclusions:
- Colloidal photonic crystals offer a promising, environmentally friendly alternative for LiDAR-assistive coatings.
- These films provide excellent durability, thermal stability, and non-toxicity.
- The developed materials significantly enhance LiDAR recognition capabilities for autonomous driving.

