Design of a self-cleanable multilevel anticounterfeiting interface through covalent chemical modulation
Creators
Abstract
Counterfeit products have posed a significant threat to consumers safety and the global economy. To address this issue, extensive studies have been exploring the use of coatings with unclonable, microscale features for authentication purposes. However, the ease of readout, and the stability of these features against water, deposited dust, and wear, which are required for practical use, remain challenging. Here we report a novel class of chemically functionalizable coatings with a combination of a physically unclonable porous topography and distinct physiochemical properties (e.g., fluorescence, water wettability, and water adhesion) obtained through orthogonal chemical modifications (i.e., 1,4-conjugate addition reaction and Schiff-base reaction at ambient conditions). Unprecedentedly, a self-cleanable and physically unclonable coating is introduced to develop a multilevel anticounterfeiting interface. We demonstrate that the authentication of the fluorescent porous topography can be verified using deep learning. More importantly, the spatially selective chemical modifications can be read with the naked eye via underwater exposure and UV light illumination. Overall, the results reported in this work provide a facile basis for designing functional surfaces capable of independent and multilevel decryption of authenticity.
Additional Information
© Royal Society of Chemistry 2023. U. M. thanks Science and Engineering Research Board (CRG/2022/000710), Ministry of Electronics and Information Technology (No. 5(1)/2022-NANO), and DBT (BT/PR45283/NER/95/1919/2022) for financial support. U. M. thanks CIF, CFN, SHST and the Department of Chemistry, Indian Institute of Technology Guwahati, for their generous assistance in executing various experiments and for the infrastructures. X. W. thanks the funding support by the startup funds of The Ohio State University. M. D. thanks the institute and MoE for her PhD fellowship. Author contributions. U. M. conceived the concept. U. M. and X. W. supervised the entire project in all aspects. M. D. carried out the major part of the experimental work with the help of S. D. and S. M.; U. I. K. performed machine learning part; Y. X., S. M., R. L. D., E. C. B., B. C. and Y. Y. helped M. D., S. D. and U. I. K. in data analysis. U. M. and X. W. wrote the manuscript with inputs from M. D., U. I. K., S. D., Y. X., S. M., R. L. D., E. C. B., B. C. and Y. Y. The authors declare no conflict of interest.Additional details
Identifiers
- Eprint ID
- 121069
- Resolver ID
- CaltechAUTHORS:20230420-711199500.19
Related works
- Describes
- 10.1039/D3MH00180F (DOI)
Funding
- Science and Engineering Research Board (SERB)
- CRG/2022/000710
- Ministry of Electronics and Information technology (India)
- 5(1)/2022-NANO
- Ministry of Science and Technology (India)
- BT/PR45283/NER/95/1919/2022
- Ohio State University
- Ministry of Education (India)
Dates
- Created
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2023-05-01Created from EPrint's datestamp field
- Updated
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2023-05-01Created from EPrint's last_modified field