Multicolor light mixing in optofluidic concave interfaces for anticounterfeiting with deep learning authentication
Anticounterfeiting technology has received tremendous interest for its significance in daily necessities, medical industry, and high-end products. Confidential tags based on photoluminescence are one of the most widely used approaches for their vivid visualization and high throughput. However, the c...
| Main Authors: | , , , , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2022
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/162307 |
| _version_ | 1824454801698586624 |
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| author | Wang, Chenlu Yan, Zhiyuan Gong, Chaoyang Xie, Hui Qiao, Zhen Yuan, Zhiyi Chen, Yu-Cheng |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Wang, Chenlu Yan, Zhiyuan Gong, Chaoyang Xie, Hui Qiao, Zhen Yuan, Zhiyi Chen, Yu-Cheng |
| author_sort | Wang, Chenlu |
| collection | NTU |
| description | Anticounterfeiting technology has received tremendous interest for its significance in daily necessities, medical industry, and high-end products. Confidential tags based on photoluminescence are one of the most widely used approaches for their vivid visualization and high throughput. However, the complexity of confidential tags is generally limited to the accessibility of inks and their spatial location; generating an infinite combination of emission colors is therefore a challenging task. Here, we demonstrate a concept to create complex color light mixing in a confined space formed by microscale optofluidic concave interfaces. Infinite color combination and capacity were generated through chaotic behavior of light mixing and interaction in an ininkjet-printed skydome structure. Through the chaotic mixing of emission intensity, wavelength, and light propagation trajectories, the visionary patterns serve as a highly unclonable label. Finally, a deep learning-based machine vision system was built for the authentication process. The developed anticounterfeiting system may provide inspiration for utilizing space color mixing in optical security and communication applications. |
| first_indexed | 2025-02-19T03:28:05Z |
| format | Journal Article |
| id | ntu-10356/162307 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2025-02-19T03:28:05Z |
| publishDate | 2022 |
| record_format | dspace |
| spelling | ntu-10356/1623072022-10-12T05:42:55Z Multicolor light mixing in optofluidic concave interfaces for anticounterfeiting with deep learning authentication Wang, Chenlu Yan, Zhiyuan Gong, Chaoyang Xie, Hui Qiao, Zhen Yuan, Zhiyi Chen, Yu-Cheng School of Electrical and Electronic Engineering Engineering::Electrical and electronic engineering Light Mixing Optofluidics Anticounterfeiting technology has received tremendous interest for its significance in daily necessities, medical industry, and high-end products. Confidential tags based on photoluminescence are one of the most widely used approaches for their vivid visualization and high throughput. However, the complexity of confidential tags is generally limited to the accessibility of inks and their spatial location; generating an infinite combination of emission colors is therefore a challenging task. Here, we demonstrate a concept to create complex color light mixing in a confined space formed by microscale optofluidic concave interfaces. Infinite color combination and capacity were generated through chaotic behavior of light mixing and interaction in an ininkjet-printed skydome structure. Through the chaotic mixing of emission intensity, wavelength, and light propagation trajectories, the visionary patterns serve as a highly unclonable label. Finally, a deep learning-based machine vision system was built for the authentication process. The developed anticounterfeiting system may provide inspiration for utilizing space color mixing in optical security and communication applications. Ministry of Education (MOE) The research of the project was supported by the Ministry of Education, Singapore, under grant AcRF TIER 1-2021-T1- 001-040 RG46/21). 2022-10-12T05:42:55Z 2022-10-12T05:42:55Z 2022 Journal Article Wang, C., Yan, Z., Gong, C., Xie, H., Qiao, Z., Yuan, Z. & Chen, Y. (2022). Multicolor light mixing in optofluidic concave interfaces for anticounterfeiting with deep learning authentication. ACS Applied Materials and Interfaces, 14(8), 10927-10935. https://dx.doi.org/10.1021/acsami.1c22466 1944-8244 https://hdl.handle.net/10356/162307 10.1021/acsami.1c22466 35172572 2-s2.0-85125391840 8 14 10927 10935 en AcRF TIER 1-2021-T1- 001-040 RG46/21 ACS Applied Materials and Interfaces © 2022 The Authors. Published by American Chemical Society. All rights reserved. |
| spellingShingle | Engineering::Electrical and electronic engineering Light Mixing Optofluidics Wang, Chenlu Yan, Zhiyuan Gong, Chaoyang Xie, Hui Qiao, Zhen Yuan, Zhiyi Chen, Yu-Cheng Multicolor light mixing in optofluidic concave interfaces for anticounterfeiting with deep learning authentication |
| title | Multicolor light mixing in optofluidic concave interfaces for anticounterfeiting with deep learning authentication |
| title_full | Multicolor light mixing in optofluidic concave interfaces for anticounterfeiting with deep learning authentication |
| title_fullStr | Multicolor light mixing in optofluidic concave interfaces for anticounterfeiting with deep learning authentication |
| title_full_unstemmed | Multicolor light mixing in optofluidic concave interfaces for anticounterfeiting with deep learning authentication |
| title_short | Multicolor light mixing in optofluidic concave interfaces for anticounterfeiting with deep learning authentication |
| title_sort | multicolor light mixing in optofluidic concave interfaces for anticounterfeiting with deep learning authentication |
| topic | Engineering::Electrical and electronic engineering Light Mixing Optofluidics |
| url | https://hdl.handle.net/10356/162307 |
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