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Abstract
Genetic analysis methods are foundational to advancing personalized medicine, accelerating disease diagnostics, and monitoring the health of organisms and ecosystems. Current nucleic acid technologies such as polymerase chain reaction (PCR) and next-generation sequencing (NGS) rely on sample amplification and can suffer from inhibition. Here, we introduce a label-free genetic screening platform based on high quality (high-Q) factor silicon nanoantennas functionalized with nucleic acid fragments. Each high-Q nanoantenna exhibits average resonant quality factors of 2,200 in physiological buffer. We quantitatively detect two gene fragments, SARS-CoV-2 envelope (E) and open reading frame 1b (ORF1b), with high-specificity via DNA hybridization. We also demonstrate femtomolar sensitivity in buffer and nanomolar sensitivity in spiked nasopharyngeal eluates within 5 minutes. Nanoantennas are patterned at densities of 160,000 devices per cm2, enabling future work on highly-multiplexed detection. Combined with advances in complex sample processing, our work provides a foundation for rapid, compact, and amplification-free molecular assays.
The authors present a high quality factor metasurface that enables sensitive and highly-parallelized detection of biomolecules. Amplification-free detection of gene fragments down to femtomolar levels is demonstrated within 5 minutes, for rapid nucleic acid analysis.
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1 Stanford University, Department of Materials Science and Engineering, Stanford, USA (GRID:grid.168010.e) (ISNI:0000000419368956)
2 Stanford University, Department of Mechanical Engineering, Stanford, USA (GRID:grid.168010.e) (ISNI:0000000419368956)
3 Stanford University, Department of Electrical Engineering, Stanford, USA (GRID:grid.168010.e) (ISNI:0000000419368956)
4 Laboratory for Solid State Physics, Zürich, Switzerland (GRID:grid.168010.e)
5 Stanford University School of Medicine, Department of Pathology, Stanford, USA (GRID:grid.168010.e) (ISNI:0000000419368956)
6 Stanford University School of Medicine, Department of Pathology, Stanford, USA (GRID:grid.168010.e) (ISNI:0000000419368956); Stanford University School of Medicine, Department of Medicine, Division of Infectious Diseases and Geographic Medicine, Stanford, USA (GRID:grid.168010.e) (ISNI:0000000419368956)
7 Stanford University School of Medicine, Department of Surgery, Stanford, USA (GRID:grid.168010.e) (ISNI:0000000419368956)
8 Washington University in St. Louis, Department of Electrical & Systems Engineering, St. Louis, USA (GRID:grid.4367.6) (ISNI:0000 0001 2355 7002)