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As a fundamental security problem, counterfeits pose a tremendous threat to public health and social economy. Herein, we exploit multi-functional nanoinks made of one-dimensional silicon-based nanohybrids for constructing fluorescent and plasmonic security tags. Of particular significance, the presented security solution exhibits triple-layer authentication model, simultaneously featuring the advantages of physical unclonable functions (PUFs), huge-encoding capacity algorithm and artificial intelligence technique. In macroscale, the multi-color fluorescence security signals are used as the first layer, which can be verified through portable smartphone. In the second security layer, the unclonable surface-enhanced Raman scattering (SERS) security signals at low-level magnification could be visualized using confocal Raman system. Taking advantages of coarse grained and quaternary encrypting of signals from Raman at each pixel, the encoding capacity reaches 6.43 × 1024082, which is much higher than the value (i.e., 3 × 1015051) ever reported. In the third layer, the aggregated SERS signals at high-level magnification Raman mapping produce unrepeatable patterns with shape-specific information. By further applying specifically artificial intelligence (AI), faint features of different SERS images are extracted and trained, allowing 98–100% of recognition accuracy after 1000 learning cycles. Such triple-layer security solution ensures the PUFs, huge encoding capacity and AI authentication simultaneously, providing newly high-performance platform of unbreakable anti-counterfeiting.

MicroRNAs (miRNAs) are promising biomarkers for the early diagnosis of breast cancer. Yet, simultaneous achievement of rapid, sensitive and accurate detection of diverse miRNAs in clinical samples is still challenging due to the low abundance of miRNAs and the complex procedures of RNA extraction and separation. Herein, we develop an innovative three-dimensional (3D) surface-enhanced Raman scattering (SERS) holography sensing strategy for rapid, sensitive and multiplexed detection of human breast cancer-associated miRNAs. To establish a proof of concept, nine kinds of human breast cancer-associated miRNAs are isothermally amplified by Exonuclease (Exo) III enzyme, and the products could be spatially separated to corresponding sensing region on silicon SERS substrates. Each region has been modified with corresponding hairpin DNA probes, which are used to identify and quantify the miRNAs. Different DNA probes are labeled with different Raman reporters, which serve as “SERS tags” to incorporate spectroscopic information into computer-generated 3D SERS hologram within ~9 min. We demonstrate that 3D SERS holography chip not only achieves an ultrahigh sensitivity down to ~1 aM but also feature a high correlation with RT-qPCR in the detection of nine miRNAs in 30 clinical serum samples. This work provides a feasible tool to improve the diagnosis of breast cancer.