Structural colour QR codes for multichannel information storage with enhanced optical security and life expectancy
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Updated: Jan 22, 2026

Quasi-light Storage for Optical Data Packets
Published on: February 6, 2014
Mahssa Abdolahi1, Hao Jiang1, Bozena Kaminska1
1School of Engineering Science, Simon Fraser University, Burnaby, British Columbia, Canada.
Abstract:
Current schemes for encoding and decoding anticounterfeiting optical quick response (QR) codes involve miscellaneous challenges. The need for using multiple light sources to read out the wavelength-multiplexed data from optically encoded organic dyes, photoblinking from quantum dots, and autofluorescence from carbon dots are some typical examples. In order to address these restrictions, we exploited our previously devised nanoimprinting-exposure-thermal-treatment (NETT) data storage approach to present a new structural-colour-based regime for optical encoding of high-security QR codes. The angle-dependent readability of our diffraction-based nanostructures poses an enhanced optical security feature that can substitute the existing inefficient encoding strategies by eliminating the constraints associated with them. Additionally, in comparison with conventional optical encoding media, using the long-lasting photocrosslinked SU-8 in the NETT method considerably enhances the life expectancy of the proposed QR codes. Also, considering the rapid NETT-based Ni stamp origination method, which was previously introduced by our group, mass-generation of the proposed codes is feasible. Owing to the special optically variable effects provided by the nanostructures, duplication of our QR codes is very difficult. The colour code design, which embeds 766 characters in 2907 modules in red, green and blue channels, was generated and fabricated onto generic nanostructure arrays using the NETT process. The encoded information was successfully read out from the pattern using a broadband light source and a digital camera. Higher capacities are also deemed to be reachable by implementing image processing and machine learning algorithms to overcome in-channel module recognition and cross-channel interferences.
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