Multispectral Quantum Dot Tags for Advanced Anticounterfeiting Applications
Source abstract
Abstract
Physical unclonable functions (PUFs) based on nanophotonic materials offer a promising route toward secure and tamper-resistant authentication. Here, we introduce a quantum dots (QDs)-driven optical fingerprinting (identifier) platform that utilizes four distinct photoluminescence (PL) emission peaks generated from two cadmium-free CIS/ZnS QDs formulations deposited side-by-side. Under multiwavelength excitation, each of them exhibits a dual-peak emission response, yielding a combined four-peak, multiexcitation spectral profile. By extracting the wavelength, full width at half-maximum, and intensity from each peak across nine excitation wavelengths, we obtain 108 independent spectral features, which are converted into a 216-bit binary fingerprint. This work incorporates a features fusion strategy that compresses multidimensional spectral descriptors into compact, discriminative digital features, enabling stable, high-entropy encoding from complex PL emission behavior. Comprehensive statistical analysis demonstrates strong uniqueness with a mean inter-Hamming distance of 0.512 ± 0.028, a wide collision margin of 99-123 differing bits, and repeatability with near-zero intra-tag variation. Bit-level randomness metrics confirm near-ideal statistical behavior after binarization. The four-peaks architecture therefore represents a significant advancement over single-peak or dual-peaks luminescent PUFs, enabling dense, high-entropy fingerprints from cadmium-free materials while remaining compatible with typical readout hardware. This work establishes a foundation for next-generation optical authentication technologies using multipeak QDs emitters.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Physical Unclonable Functions (PUFs) and Hardware Security · Random lasers and scattering media · Gold and Silver Nanoparticles Synthesis and Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Electronics and Computer Science
University of Southampton
Funding
Electronics and Computer Science, University of Southampton · Engineering and Physical Sciences Research Council
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