Q-Switching Nanophotonic Biosensing

Q-Switching Nanophotonic Biosensing | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Q-Switching Nanophotonic Biosensing Liaoyong Wen, Jiacheng Sun, Fajun Li, Xudong Wang, Sisi Yan, Jing He, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7176623/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Sensitive, label-free detection of biomarkers is critical for clinical diagnostics. However, conventional nanophotonic biosensors, typically based on single-oscillator architectures, remain confined to either real or imaginary sensing domain. This isolation often results in weak signal responses, limited operational stability, and high instrumental complexity. We introduce a Q -switching sensing mechanism based on strongly coupled-oscillators that bridges the real and imaginary domains of nanophotonic biosensing. This mechanism amplifies subtle variations in the real part of the refractive index into pronounced switching of the radiative quality factor, enabling robust, intensity-based signal readout. The Q -switching sensing chip is implemented in a defect-tolerant, nonlocal three-dimensional bound-state-in-the-continuum metasurface, fabricated via aluminum-based lithography on 8-inch wafers. As a result, it achieves lattice-independent peak sensitivity exceeding 10 3 %/RIU across the visible, near-infrared, and short-wave infrared regimes, an order of magnitude improvement over conventional refractometric biosensors. Integrated into a point-of-care testing system, this handheld, diode-driven Q -switching sensing platform enables rapid detection of small extracellular vesicles at concentrations as low as 24 attomolar, offering a 10 4 -fold sensitivity enhancement over the mainstream ELISA for postoperative lung cancer monitoring. Grounded in Q -switching physics, this strategy offers a scalable, high-performance biosensing platform for portable diagnostics in clinical, remote, and at-home settings. Physical sciences/Optics and photonics/Applied optics/Optical sensors Physical sciences/Optics and photonics/Optical physics/Nanophotonics and plasmonics Physical sciences/Nanoscience and technology/Nanoscale devices/Nanophotonics and plasmonics Physical sciences/Optics and photonics/Optical materials and structures/Metamaterials Physical sciences/Optics and photonics/Applied optics/Optoelectronic devices and components Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Detecting biomarkers with high sensitivity is crucial for advancing medical diagnostics, particularly in enabling real-time disease monitoring without reliance on fluorescent or radioactive labels 1,2 . Nanophotonic biosensors have emerged as critical analytical tools, typically employing a single-oscillator that detects and responds separately to either the real 3-10 or imaginary 11-15 components of the refractive index ( Fig. 1a , first and third quadrants). Among them, refractometric biosensors uniquely operate in a label-free and real-time manner by translating subtle refractive index changes ( Δn ) into resonance frequency shifts ( Δω ), offering broad compatibility with diverse biomolecular interactions and thus making them widely regarded for probing biomarkers in both fundamental research and clinical application 16-22 . However, the inherently small Δn at the nanoscale yields subtle spectral shifts, which limit signal clarity and require complex instrumentation, thereby constraining the scalability of these approaches 1,22 . Although recent advances in nanophotonic devices have introduced non-radiative optical modes that hold promise for enhancing refractometric sensing by enabling narrowband resonances and strong light-matter interactions 23-29 , these approaches often involve suppressing radiative losses while maintaining fixed quality factors ( Q -factor) without energy radiation during sensing ( Fig. 1b ). As a result, signal intensity is inevitably diminished with high Q -factor, requiring increasingly sophisticated optical setups to detect minor spectral shifts ( Fig. 1c ) 22,30,31 . Moreover, the precise and costly fabrication required for these fragile high quality resonators, combined with their sensitivity to structural defects, presents further barriers to scalable deployment 1,32,33 . Together, these challenges underscore the urgent need for a fundamentally new sensing strategy that can robustly amplify weak refractive index changes into clearly distinguishable, low-complexity optical outputs. Here, we introduce a Q -switching sensing mechanism based on coupled-oscillators that circumvents the intrinsic physical barrier among single-oscillator biosensors by directly linking the real part of the refractive index ( Δn ) with the imaginary component of the optical frequency ( Δiγ ). While Q -switching is a well-established approach in laser physics, its biosensing analogue has remained largely unexplored. In this framework, biomarker binding induces a rapid Q -factor switching, generating amplified, intensity-based signals ( ΔI ). The Q- switching sensing chip is implemented by a three-dimensional bound-state-in-the-continuum (3D BIC) metasurface that supports nonlocal, Q -switchable modes within a strong coupling system. This architecture achieves a lattice-independent peak sensitivity exceeding 10 3 %/RIU across the visible, near-infrared, and short-wave infrared regimes in experiment, representing an order-of-magnitude enhancement over conventional refractometric biosensors. Fabricated using a wafer-scale, aluminum-based 3D lithography (AL-3Dlitho) process, the defect-tolerant nonlocal metasurface demonstrates high scalability across 8-inch substrates, with a resonance deviation of less than 0.97 nm. Integrated into a compact, diode-driven point-of-care testing (POCT) platform, the Q -switching sensing system enables rapid and ultrasensitive detection of small extracellular vesicles (sEVs) down to 24 attomolar, corresponding to a 10⁴-fold improvement in sensitivity compared to commercial enzyme-linked immunosorbent assays (ELISA). In clinical validation, the diagnostic tests achieve a 93.5% area under the curve (AUC) of receiver operating characteristic (ROC) for postoperative lung cancer monitoring. By establishing a previously untapped sensing mechanism, this work establishes a versatile and scalable platform for next-generation, high-performance diagnostic technologies. Q -switching sensing mechanism Coupled-oscillators were introduced to unify single-oscillator sensing mechanisms across both the real and imaginary domains, thereby extending the operational regime into the second and fourth quadrants of the complex sensing space ( Fig. 1a ). This study focuses on the third-quadrant regime, referred to as Q -switching sensing. The intrinsic quality factor of the system, defined as Q = (1/ Q r + 1/ Q n ) -1 , reflects the balance between radiative (1/ Q r ) and non-radiative (1/ Q n ) loss channels 34 . Here, Q n is determined by material absorption or intrinsic dissipation, while Q r is sensitive to external perturbations such as biomarker binding. During Q -switching sensing, variations in the ambient refractive index predominantly affect Q r , with negligible influence on Q n . The dependence of Q r on Δn in the strong coupling system is governed by the relation: Development of 3D BIC metasurface for Q -switching sensing To optimize Q -switching sensing performance, precise control over radiative channels in a strong coupling BIC system is essential. Conventionally, in-plane geometric asymmetry ( ΔD > 0, ΔZ = 0) has been employed to modulate radiative energy leakage in a strong coupling system 36-38 . However, this approach induces crosstalk among multiple quasi-BIC (qBIC) resonances within the strong coupling system due to the geometry-sensitive nature of the modes (Supplementary Note S2 and Fig. S6), resulting in unstable resonance frequencies and fluctuating radiative energy behavior ( Fig. 2a, Supplementary Note S3 and Fig. S7a). As an alternative, we introduce out-of-plane spatial asymmetry by vertically displacing the constituent nanoparticles ( ΔD = 0, ΔZ > 0) ( Fig. 2b ). This design preserves the dipole moment of the nanoparticles, stabilizes the resonance frequencies, and enables controllable Q -factor tuning for both the upper (qBIC U ) and lower (qBIC L ) branches of the qBIC resonances. Extending this symmetry breaking into three dimensions, forming a “3D BIC metasurface”, provides a robust mechanism for modulating radiative losses in multimode, strong coupling (Supplementary Fig. S7b-d). More detailed optimization procedures and derivations are provided in Supplementary Note S3 and Figs. S8–S13. For the fabrication of the 3D BIC metasurfaces, an AL-3Dlitho technique was employed, which enabled precise and scalable control of out-of-plane displacement 9,39,40 . This method firstly used binary-pore anodic aluminum oxide templates to construct 3D mother silicon nanostructures ( Fig. 2c ), followed by a lift-off process to transfer deposited gold films on the silicon substrate, resulting in the final 3D BIC metasurfaces. Scanning electron microscopy (SEM) and atomic force microscopy (AFM) confirmed the successful realization of dual-layer gold nanoparticle arrays with a vertical displacement of 140 nm ( Fig. 2d ). Detailed fabrication procedures and morphological characterizations are provided in the Materials and Methods section MM2 and Supplementary Figs. S14-S16. Optical measurements (Materials and Methods section MM3 and Supplementary Fig. S17) revealed hallmark features of qBIC-coupled resonances, where their linewidths transition from undetectable to broad as ΔZ increases from 0 to 140 nm ( Fig. 2e ). These experimental observations closely match the numerical simulations. To account for fabrication-induced losses, the simulated spectra incorporate a 1.2-fold enhancement in the imaginary component of the gold refractive index, Im(n Au ) . Despite this adjustment, the qBIC U exhibits minimal frequency shift, underscoring its intrinsic robustness to material loss (Supplementary Fig. S18). Deposition of TiO₂ film on the 3D metasurface with ΔZ = 140 nm further demonstrates a strong coupling behavior, with clear Rabi splitting evident in the reflectance spectra ( Fig. 2f) . The onset of a monotonic increase in qBIC U intensity at t TiO₂ = 80 nm closely mirrors the simulated response. Performance characterization of Q -switching sensing chip The Q -switching sensing mechanism exhibits distinct behavior compared to conventional refractometric sensing, both in near-field and far-field characterization. As shown in Fig. 3a , refractometric sensing relies on complete near-field overlap between the analyte and the optical mode, maximizing the mode overlap factor to induce a resonance shift 1,3 . However, such near-field localization usually leads to increased energy dissipation and a lower Q factor 41,42 . Q -switching sensing can achieve remote detection by spatially separating the analyte (in the superstrate) from the optical mode (in the substrate) during the sensing process. As a result, a high- Q nonlocal mode can be efficiently switched by a Δn , producing amplified energy fluctuations without requiring strong near-field overlap with the analyte, thereby preserving its radiationless character and high sensitivity. To validate the universality of Q -switching sensing, we conducted both theoretical simulation and experimental measurements on metasurfaces with periodicities of 470, 600, and 800 nm, assessing their far-field bulk refractive index sensitivity across the visible (VIS), near-infrared (NIR), and short-wavelength infrared (SWIR) spectral regions ( Fig. 3b and Supplementary Figs. S19–S21). The frequency shift of the qBIC L , which follows conventional refractometric sensing principles, diminishes rapidly at shorter wavelengths due to smaller lattice constants ( Fig. 3c ). Conversely, the initial Q r of the qBIC U increases at shorter wavelengths and undergoes faster switching toward the corresponding Q n ( Fig. 3d ), resulting in a lattice-independent intensity response in Q -switching sensing ( Fig. 3e ). Experimentally extracted Q n values in the VIS, NIR, and SWIR bands are 58.6, 64.2, and 115, respectively, with detailed fitting parameters provided in Supplementary Fig. S20. Metasurface-based refractometric biosensors are fundamentally limited not only by their lattice constants but also by their Q factors. Achieving a high Q factor in conventional refractometric designs typically requires strong optical mode confinement within the resonator, which paradoxically reduces near-field overlap with the analyte and thereby limits sensitivity 22,43,44 . As shown in Fig. 3f , the normalized peak sensitivity ( S λ / λ 0 ) of various metasurfaces operating with constant Q factors decreases as the Q factor increases, remaining below 10² %/RIU 45-52 . In contrast, Q -switching sensing chips exhibit normalized peak sensitivities ( S I / I 0 ) on the order of ~10³ %/RIU across the VIS, NIR, and SWIR bands in experiment, representing an order-of-magnitude enhancement. These findings underscore the unique capability of Q -switching sensing to resolve the longstanding intrinsic trade-off between energy dissipation and sensitivity in frequency-shift-based single-oscillator biosensors, establishing it as a spectrally versatile approach for broadband biochemical detection. Miniaturization and Scalability of Q -switching sensing system Conventional spectrometer-free biosensing systems based on high- Q resonators typically demand narrowband, frequency-aligned light sources, imposing stringent requirements on the optical setup. These constraints lead to bulky, complex instruments, as minimal resonance mismatch often fail to generate sufficiently detectable signals 26,53,54 . Q -switching sensing systems achieve superior signal responses and enhanced detectability while dramatically simplifying the optical configuration, requiring only an incoherent light source such as a broadband optical filter or LED. To demonstrate this advantage, gradient SiO 2 bars with thicknesses of 10, 20, and 30 nm were sequentially deposited onto the Q -switching sensing chip using photolithographic overlay techniques ( Fig. 4a and Supplementary Fig. S22). The nanoscale morphology was reconstructed under both coherent narrowband illumination (2 nm bandwidth) and incoherent broadband illumination (10 and 25 nm bandwidths) ( Fig. 4b ). Under narrowband conditions, slight wavelength deviations (in step of 6 nm) led to substantial reconstruction errors, highlighting the limitations of narrowband-resonance-based imaging (Supplementary Fig. S23). While reconstructions obtained using broadband filter and LED illumination closely matched the ground truth of the gradient pattern, demonstrating high accuracy and reliability. These results underscore the potential of Q -switching sensing systems, which integrate an LED, photodetector, 3D BIC metasurface, and beam splitter into a handheld platform ( Fig. 4c ). Furthermore, the distinctive design of the 3D BIC metasurface allows for scalable fabrication via AL-3Dlitho on 6- and 8-inch wafers ( Fig. 4d ). A key advantage of this system lies in the exceptional robustness of the nonlocal Q -switchable mode against fabrication defects ( Fig. 4e ). When a ΔD ( D A − D B ) variation is introduced between binary nanoparticles with a 600 nm period, the low- Q local mode exhibits significant wavelength shifts in response to the defect. In contrast, the high- Q nonlocal mode remains remarkably stable, maintaining a center wavelength near 940 nm with only a 0.97 nm deviation in wafer-scale samples. The combination of the Q -switching sensing mechanism with a scalable 3D BIC metasurface (as low as 5 USD per chip) offers a highly integrated, cost-effective commercialized sensing system (Table S1). Despite its compact footprint, the miniaturized system effectively translates Q -switching optical responses into prominent electrical signal variations, achieving a limit of detection (LOD) of 1.1486 × 10⁻⁵ RIU ( Fig. 4f and Supplementary Fig. S24). This performance rivals the leading capabilities of conventional high- Q metasurfaces 55 and other miniaturized SPR biosensors 56 . To demonstrate its practical effectiveness, we tested different concentrations of IgG using the miniaturized Q -switching sensing system, applying a 5-minute binding period followed by a 10-minute buffer wash. The calculated dissociation rate constant (K D ) was 3.125 nM ( Fig. 4g ). These findings confirm the potential of our system as a high-performance platform for the rapid and sensitive evaluation of biomolecular binding events. Clinical application of Q -switching sensing system To demonstrate the clinical translation potential of our Q -switching POCT sensing system, we conducted a proof-of-concept study targeting lung cancer (LC), one of the leading causes of cancer-related mortality worldwide ( Fig. 5a ) 57 . sEVs were selected as biomarkers due to their growing significance in liquid biopsy approaches 58,59 . LC cells actively secrete sEVs via multivesicular endosome–plasma membrane fusion, and their presence has been confirmed in multiple biofluids 60 . Nanoparticle tracking analysis revealed a unimodal size distribution centered around ~120 nm (Supplementary Fig. S25). These sEVs encapsulate tumor-associated molecular cargos, including established markers such as CD63 and CD151, highlighting their diagnostic relevance. To interrogate sEV membrane proteomic profiles, we engineered Q- switching sensing chips with ligand-specific immobilization strategies (see Materials and Methods section MM4 and Supplementary Fig. S26). Optical signal intensity increased markedly from the bare metasurface to sEVs binding, indicating strong and specific molecular recognition ( Fig. 5b and Supplementary Fig. S27). SEM imaging further confirmed the selective capture of sEVs on the CD63-functionalized metasurface ( Fig. 5c ). Notably, our sensing system enabled rapid screening of native samples with varying sEV concentrations by monitoring real-time intensity changes within 15 minutes ( Fig. 5d ). Quantitative analysis using four-parameter logistic regression established a detection limit of 72 sEVs (~24 aM), representing a remarkable 10⁴-fold sensitivity enhancement over the conventional ELISA (which require ~6 × 10⁵ sEVs) ( Fig. 5e and Supplementary Fig. S28). To validate the clinical utility of our approach, we conducted a case-control study using serum samples from 34 lung cancer (LC) patients and 22 age-matched healthy donors ( Fig. 5f and Table S2). High-throughput Q -switching sensing of CD63 and CD151 expressions in clinical samples revealed significant distinguishment between LC and healthy controls (Fig. 5g ), demonstrating excellent diagnostic performance. To further assess prognostic potential, we analyzed serum from 16 favorable- and 12 poor-prognosis patients ( Fig. 5h, top). For comparison, conventional ELISA was performed to quantify total protein levels ( Fig. 5h, bottom , and Materials and Methods section MM4). Unlike ELISA, which lacks specificity for vesicle-bound markers, Q -switching sensing directly targeted sEV-associated markers, revealing distinct expression patterns aligned with disease status, consistent with prior reports 61-64 . Magnetic resonance imaging (MRI) scans before and after treatment further validated these clinical outcomes, showing tumor regression and reduced ground-glass opacities in responders, while non-responders exhibited clear disease progression ( Fig. 5i and Supplementary Fig.S29). We developed regression-based scoring models from Q -switching sensing and ELISA data to evaluate their prognostic performance using receiver operating characteristic analysis ( Fig. 5j ). Notably, the Q -switching sensing significantly outperformed ELISA, achieving an AUC of 93.2%, compared to 69.3%. These findings underscore the superior analytical power of the sEV-targeted Q -switching assay, which captures disease-relevant biological signals grounded in cellular origin and vesicle-mediated mechanisms. By enabling highly sensitive and precise detection of vesicle-derived biomarkers, the Q -switching sensing offers strong potential for advancing early cancer diagnosis and longitudinal disease monitoring across multiple cancer types. Conclusion We report a Q -switching sensing mechanism based on coupled-oscillators, which fundamentally overcomes the intrinsic limitations of conventional single-oscillator sensors by converting subtle refractive index variations into amplified optical intensity signals. This mechanism is realized via a 3D BIC metasurface with engineered out-of-plane asymmetry, enabling robust excitation of nonlocal, Q -switchable modes under strong coupling. The resulting Q -switching sensing chips exhibit lattice-independent, broadband responses spanning the VIS to SWIR regimes and are readily integrated into compact, diode-driven systems. It achieves a refractive index detection limit of 10⁻⁵ RIU and enables ultrasensitive detection of small extracellular vesicles at concentrations as low as 24 aM, a >10⁴-fold improvement over standard ELISA. In clinical studies, the system demonstrated a diagnostic AUC of 93.2%, highlighting its translational potential. Rather than suppressing radiative losses, Q -switching sensing harnesses them, facilitating loss-driven nanophotonic biosensing in complex space. This approach offers a highly sensitive, portable, and scalable solution for next-generation diagnostics in clinical, point-of-care, and resource-limited settings. Declarations Acknowledgments: The authors thank the facility support and technical assistance from the Westlake Centre for Micro/Nano Fabrication, the Instrumentation and Service Centre for Physical Sciences (ISCPS), and the Instrumentation and Service Centre for Molecular Sciences (ISCMS) at Westlake University. Funding: Natural Science Foundation of China (Grants No. 52373238, 52003225, 62175205, and U2130112) Research Centre for Industries of the Future at Westlake University (RCIF, Grant No. WU2022C024) Special Support Plan for Photoelectric Chips Research at Westlake University (Grant No. 10300000H062201) Key Project of Westlake Institute for Optoelectronics (Grant No. 2023GD005) and Westlake Education Foundation The Youth Talent Support Program of Fujian Province (Eyas Plan of Fujian Province) [2022] Author contributions: J.C.S. and L.Y.W. conceived the idea. J.F.Z. and L.Y.W. supervised the project. M.Q. provided helpful discussions. J.C.S. implemented the theoretical analysis, simulation, fabrication, and optical measurement. J.C.S. and J.H. built the measurement setup. F.J.L. and S.W.L. provided the bio-samples. X.D.W. and F.J.L. implemented the microfluidic biosensing test and data collection. J.C.S., F.J.L., X.D.W., S.S.Y., D.W.N., and L.W. performed the data processing and analysis. J.C.S. and L.Y.W. wrote the manuscript with input from J.F.Z., F.J.L., and M.Q. All authors contributed to the manuscript and approved the final version. Competing interests: L.Y.W., J.C.S., S.S.Y. and X.D.W. have a pending patent application related to the Q -switching nanophotonic biosensing used in this paper, described in a Chinese patent through Westlake University (Chinese patent no. CN 119376096 A) for technology related to Q -switching sensing physics, chip fabrication and its integrated sensing system. The other authors declare no competing interests. Data and materials availability: All data are available in the main text or the supplementary References H. Altug, S.-H. Oh, S. A. Maier, J. Homola, Advances and applications of nanophotonic biosensors. Nature Nanotechnology 15 , 5–16 (2022). A. V. Kabashin, V. G. Kravets, A. N. 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Nature Nanotechnology , (2025). Materials and Methods MM1: Numerical simulation Finite-difference time-domain (FDTD) simulations were performed using the Lumerical FDTD software package. A uniform mesh size of 5 nm was applied following a convergence test to ensure accuracy. Periodic boundary conditions were implemented in the x and y directions to model a square periodic array, while perfectly matched layers (PMLs) were applied along the z direction to absorb outgoing waves. A plane-wave source propagating along the z-axis was used for illumination. The optical constants of gold (Au) were taken from Johnson and Christy. The refractive indices of SiO₂ and the quartz substrate were set to n = 1.5, TiO₂ to n = 2.2, and NOA83 to n = 1.56. MM2: Fabrication and characterization of 3D BIC metasurfaces Fabrication of the BP-AAO Template : The preparation of the binary-porous anodic aluminum oxide (BP-AAO) template involved several key steps. First, a nickel film with periodic nanopillars (period P = 800 nm) was used to imprint an electropolished aluminum foil under a pressure of 15 kN cm⁻² for 3 minutes, generating a nanodot array with 800 nm spacing on the aluminum surface. The imprinted foil was then anodized at 320 V in a mixed electrolyte solution (comprising 4 g citric acid, 200 mL ethylene glycol, 200 mL deionized water, and 10 mL of 0.1 wt% H₃PO₄) at 30 °C for 2 hours, forming a highly ordered array of primary nanopores (A-pores). Subsequently, the A-pores were widened by immersing the aluminum foil in a 5 wt% H₃PO₄ solution for 2 hours. To protect the pore structure during further processing, a conformal TiO₂ layer was deposited via atomic layer deposition (ALD) using a D100-4882 system (Yaona Electronics) at 150 °C for 70 cycles. Each ALD cycle consisted of 0.5 s exposure to titanium isopropoxide (Ti precursor, C₁₂H₂₈O₄Ti), 8 s N₂ purge, 0.1 s H₂O pulse, and another 8 s N₂ purge. The top surface of the BP-AAO was then milled using an ion beam milling system (IM4000plus) to fully open the B-pores. A PMMA layer was spin-coated on the surface, and the unoxidized aluminum foil was removed using a mixed etchant containing 1.5 wt% CuCl₂ and 53.2 wt% HCl. After etching, the PMMA layer was dissolved in acetone. Finally, the template was immersed in 0.1 M NaOH solution for 30 minutes at room temperature to generate secondary nanopores (B-pores) at the fourfold junctions of the A-pores. The obtained BP-AAO template was transferred to a silicon wafer using water as the transfer medium. Fabrication of the 3D Nanostructure on Silicon : The silicon wafer was first etched using an inductively coupled plasma (ICP) system (Leuven ICP, HAASRODE-E200A) for 20 seconds with a gas mixture of CF₄ and SF₆, under conditions of 20 °C and 8 mT VAT. Tilted ion beam milling was employed to remove the barrier layer at the base of the A-pores (Fig. S14a–c). A 10 nm thick Cr sacrificial layer was deposited via physical vapor deposition (PVD) (Fig. S14d), after which the BP-AAO template was peeled off, revealing a bilayer Cr particle structure (Fig. S14e). A second ICP etch was then conducted using the same gas mixture, with a 24-second etch time, to form the final 3D silicon nanostructure (Fig. S14f). A 50 nm gold (Au) layer was deposited by PVD, followed by NOA83 coating, enabling lift-off of the 3D nanostructure and formation of the 3D bound-state-in-continuum (BIC) metasurface (Figs. S14g, S14h). Additional morphology and fabrication details are shown in Fig. S15 (pre-2nd etch) and Fig. S16a (post-etch). Fig. S16b displays the final gold-coated 3D BIC metasurface. Characterization Techniques: The nanoscale morphologies presented in Fig. 2c and Fig. 2d , Supplementary Fig. S15, and Fig. S16 were characterized using an analytical field-emission scanning electron microscope (FE-SEM, Zeiss Gemini 450). Atomic force microscopy (AFM, Bruker Dimension ICON) was employed to obtain the topographical image shown in Fig. 2d bottom. Microscopic patterns were analyzed using a metallographic microscope (CEWEI LW750LJT). Lithographic Overlay Method : The lithographic overlay shown in Fig. 4a was implemented using a semi-automated mask aligner (MA/BA6 Gen4, SUSS MicroTec). ARP5350 photoresist (Allresist GmbH) was used for overlay lithography. For extracellular vesicles characterization, standard high-speed centrifugation protocols were used to isolate and purify serum-derived small extracellular vesicles. Their morphology was examined using a transmission electron microscope (TEM, HT-7700, Hitachi, Japan) operated at 100 kV. MM3: Optical setup To measure the reflectance spectra and acquire hyperspectral images of the 3D BIC metasurfaces, we developed a custom dual optical path system (Supplementary Fig. S17). The visible (VIS) path covers 400–1000 nm, while the near-infrared (NIR) path spans 1000–1700 nm. A broadband supercontinuum laser (SC-5, YSL Photonics) serves as the illumination source, delivering 470–2400 nm output with > –10 dBm/nm power spectral density and < 0.1 dB spectral power jitter. In both paths, a beam expander (25 mm and 150 mm focal length lenses) ensures uniform illumination. Reflective Köhler illumination is achieved using a 250 mm focal length lens pair (L1 and L2) in combination with a beam splitter. In the VIS path, hyperspectral imaging is realized via a laser line tunable filter (LLTF CONTRAST™, Photon etc.) based on volume holographic gratings, offering >OD6 out-of-band rejection and FWHM bandwidth of ~1.75 nm at 700 nm (up to 2.5 nm at 1000 nm). A high-resolution VIS camera (Digital Sight 50M, Nikon; 3.76 μm pixel size, 85% peak quantum efficiency) and a fiber spectrometer (TREX, TAIZI Technology; 200–1000 nm) capture reflectance data. The NIR path employs a semiconductor-cooled camera (SWIR1300KMA, ToupTek; 1.31 MP, 400–1700 nm) and a high-resolution spectrometer (AQ6370D, YOKOGAWA; 0.02 nm resolution, 600–1700 nm, +20 to –90 dBm detection range) for precision resonance detection. To streamline functionality, a shared optical cage system—co-developed with Ray Cage (Zhenjiang) Photoelectric Technology Co. Ltd—accommodates both VIS and NIR paths on a common objective stage. This configuration supports simultaneous reflectance/transmission measurements and hyperspectral imaging in both domains, with illumination and detection possible from either side of the sample, significantly enhancing system versatility. MM4: Biofunctionalization and serum detection analysis Surface functionalization and biosensing Assay: The Q- switching sensing chips were functionalized via covalent surface chemistry using (3-glycidoxypropyl) trimethoxysilane (3-GPS, Sigma-Aldrich, USA) for antibody immobilization. Cleaned metasurfaces were incubated in 1% (v/v) 3-GPS in toluene for 20 minutes, rinsed in fresh toluene, dried under nitrogen, and baked at 120 °C for 30 minutes to form a uniform silane monolayer on the silicon oxide surface. The terminal epoxide group of 3-GPS covalently binds to the amine groups of antibodies. CD151 capture antibodies (Thermo Fisher, USA) were immobilized directly onto the epoxy-silane-coated surfaces. To block nonspecific binding, the functionalized metasurfaces were incubated in 50 μg/mL bovine serum albumin (BSA, Sangon Biotech, China) for 30 minutes, followed by two PBS washes (5 min each). The resulting antibody-functionalized metasurfaces were then used to detect serum-derived extracellular vesicle (EV) membrane proteins. ELISA benchmarking assay: A standard sandwich ELISA was conducted for comparison. Capture antibodies against CD63 and CD151 were immobilized on 96-well plates (SenBeiJia Biological) and blocked with 1% BSA. Clinical serum samples were thawed on ice, fixed with 4% paraformaldehyde, and permeabilized using 0.1% Triton X-100 for 15 min at room temperature. After treatment, 50 μL of each sample was added to the functionalized plates and incubated overnight at 4 °C. Plates were washed three times with PBS containing 0.05% Tween-20, incubated with HRP-conjugated secondary antibodies, and developed using a chemiluminescent substrate. Signal intensity was quantified using a microplate reader (Tecan). Data Analysis and Ethics: All measurements were performed in triplicate, and results are reported as mean ± standard deviation (s.d.). The number of replicates (N) is indicated in each statistical analysis. Significance between groups was assessed using a two-tailed Student’s t-test, with adjusted P-values < 0.001 considered statistically significant. Data analysis was performed using Origin software. All procedures involving human samples were approved by the Ethics Committee of the First Affiliated Hospital of Xiamen University (approval number: XMFHIIT-2024SL017). Written informed consent was obtained from all participants. Clinical sample collection and analysis adhered to the principles of the Declaration of Helsinki. Additional Declarations Yes there is potential Competing Interest. L.Y.W., J.C.S., S.S.Y. and X.D.W. have a pending patent application related to the Q-switching nanophotonic biosensing used in this paper, described in a Chinese patent through Westlake University (Chinese patent no. CN 119376096 A) for technology related to Q-switching sensing physics, chip fabrication and its integrated sensing system. The other authors declare no competing interests. Supplementary Files supplementarymaterial2025finalnaturephotonics.docx supplementary material for Q-Switching Nanophotonic Biosensing Cite Share Download PDF Status: Under Review Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7176623","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":488895551,"identity":"be210a6e-c690-4f21-9c2a-bf8b97ab5d63","order_by":0,"name":"Liaoyong Wen","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0003-3185-575X","institution":"Westlake University","correspondingAuthor":true,"prefix":"","firstName":"Liaoyong","middleName":"","lastName":"Wen","suffix":""},{"id":488895552,"identity":"1bdd3c53-40b7-4789-a0ce-bb4087b899fa","order_by":1,"name":"Jiacheng Sun","email":"","orcid":"","institution":"Westlake University","correspondingAuthor":false,"prefix":"","firstName":"Jiacheng","middleName":"","lastName":"Sun","suffix":""},{"id":488895553,"identity":"9d1c077c-5e2d-4ab8-aa20-af5cae520115","order_by":2,"name":"Fajun Li","email":"","orcid":"","institution":"Xiamen University","correspondingAuthor":false,"prefix":"","firstName":"Fajun","middleName":"","lastName":"Li","suffix":""},{"id":488895554,"identity":"34588838-9962-490f-9630-6a8a13456409","order_by":3,"name":"Xudong Wang","email":"","orcid":"","institution":"Westlake Institute for Optoelectronics","correspondingAuthor":false,"prefix":"","firstName":"Xudong","middleName":"","lastName":"Wang","suffix":""},{"id":488895555,"identity":"7a2d0661-f435-4d72-a68a-5374167c5d0f","order_by":4,"name":"Sisi Yan","email":"","orcid":"","institution":"Westlake University","correspondingAuthor":false,"prefix":"","firstName":"Sisi","middleName":"","lastName":"Yan","suffix":""},{"id":488895556,"identity":"3a0bbdfa-fb82-4497-bd09-6eb492eec326","order_by":5,"name":"Jing He","email":"","orcid":"","institution":"Westlake University","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"He","suffix":""},{"id":488895557,"identity":"461ea24b-6bda-4b48-a8b3-b9edba7ca2a8","order_by":6,"name":"Dangwu Ni","email":"","orcid":"","institution":"Westlake University","correspondingAuthor":false,"prefix":"","firstName":"Dangwu","middleName":"","lastName":"Ni","suffix":""},{"id":488895558,"identity":"18024b18-c4bb-4dd8-95af-3f4383f75f3c","order_by":7,"name":"Lang Wang","email":"","orcid":"","institution":"Westlake University","correspondingAuthor":false,"prefix":"","firstName":"Lang","middleName":"","lastName":"Wang","suffix":""},{"id":488895559,"identity":"f160d2a3-d20d-40e2-a9ac-e5ca7547a597","order_by":8,"name":"Shaowei Lin","email":"","orcid":"","institution":"The First Affiliated Hospital of Xiamen University School of Medicine Xiamen University","correspondingAuthor":false,"prefix":"","firstName":"Shaowei","middleName":"","lastName":"Lin","suffix":""},{"id":488895560,"identity":"5f20276f-b01a-4906-8420-cb4dd29a37b3","order_by":9,"name":"Min Qiu","email":"","orcid":"https://orcid.org/0000-0002-4613-5125","institution":"Westlake University","correspondingAuthor":false,"prefix":"","firstName":"Min","middleName":"","lastName":"Qiu","suffix":""},{"id":488895561,"identity":"1e55dbfd-cebe-49d7-95f3-076b235ff9df","order_by":10,"name":"Jinfeng Zhu","email":"","orcid":"https://orcid.org/0000-0003-3666-6763","institution":"Xiamen University","correspondingAuthor":false,"prefix":"","firstName":"Jinfeng","middleName":"","lastName":"Zhu","suffix":""}],"badges":[],"createdAt":"2025-07-21 11:00:43","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7176623/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7176623/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87392340,"identity":"2cf1cc8c-143b-4ed6-889b-029a74d32374","added_by":"auto","created_at":"2025-07-23 10:03:09","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":268274,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eQ-switching sensing mechanism in the coupled-oscillators. (a) Illustration of nanophotonic biosensing mechanisms across four quadrants: the first quadrant represents refractometric biosensing, and the third quadrant denotes surface-enhanced spectroscopic sensing, both based on single-oscillators. The second and fourth quadrants illustrate underexplored complex sensing domains accessible through coupled-oscillators with coupling strength g. (b) Comparison of system energy responses to refractive index changes (Δn) between conventional refractometric sensing (a fixed Q-factor without energy radiation during sensing) and Q-switching sensing (a switchable Q-factor with energy radiation during sensing). (c) Relation between signal intensity and Q factor for refractometric sensing and Q-switching sensing. (d) Analytical calculations for Q-switching sensing within a strong coupling system, demonstrating that, given a specific nonradiative Q-factor (Qₙ), the radiative Q-factor (Qᵣ) decreases rapidly in response to Δn, with varying initial decay rates (γr₀). (e) Numerical simulations of spectral responses to Δn in a strong coupling system reveal that the lower branch (oscillator 1) undergoes subtle frequency shifts, while the upper branch (oscillator 2) exhibits an amplified intensity response to refractive index perturbations. (f) Schematic of a miniaturized, diode-driven Q-switching sensing system utilizing a 3D BIC metasurface, enabling narrowband resonance compatibility with broadband light sources.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7176623/v1/061d0f07c124863ef4ba83eb.png"},{"id":87392343,"identity":"65688492-a67d-43f5-960c-1483b66b1ab2","added_by":"auto","created_at":"2025-07-23 10:03:09","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":258334,"visible":true,"origin":"","legend":"\u003cp\u003eDesign and characterization of 3D BIC metasurface for Q-switching sensing. (a, b) Simulated spectral evolution demonstrating radiative loss control through symmetry breaking: (a) in-plane asymmetry achieved by varying particle diameters (ΔD = DA − DB, DA = 300 nm); (b) out-of-plane asymmetry introduced via longitudinal displacement differences (ΔZ = ZA − ZB, ZA = 200 nm). (c) Scanning electron microscopy (SEM) image of a 3D silicon template fabricated using a binary-pore AAO template before the second ICP etching process. (d) SEM and atomic force microscopy (AFM) characterizations of the fabricated 3D BIC metasurface exhibiting a ΔZ of 140 nm. (e) Comparison of measured and simulated reflectance spectra of 3D BIC metasurfaces immersed in water with different ΔZ values. (f) Measured reflectance spectra from the metasurface with ΔZ = 140 nm, using tTiO2 ranging from 0 to 150 nm, highlight the spectral response characteristic of the strong coupling system.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7176623/v1/3661b1576e3b20fa94369035.png"},{"id":87393303,"identity":"b9fb62d8-d1a2-48c0-89f9-37f5b594a572","added_by":"auto","created_at":"2025-07-23 10:11:09","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":334101,"visible":true,"origin":"","legend":"\u003cp\u003eNear-field and far-field performance of Q-switching sensing chip. (a) Schematic comparison of near-field light matter interactions in conventional refractometric sensing versus Q-switching sensing, highlighting the distinct differences in field distributions. (b) Measured far-field absorption spectra of 3D BIC metasurfaces with periodicities of 800 nm (SWIR), 600 nm (NIR), and 470 nm (VIS) reveal distinct spectral responses of the upper and lower branches to bulk refractive index variations (Δn). (c) Wavelength shift of the lower branch in the coupled-oscillators as Δn increases, measured across the three spectral bands. (d) Calculated Qr values of the upper branch, derived from measured spectra, exhibit exponential decay with increasing Δn. The fitted equations for each spectral band are: Qr = 820×e-x/0.011 + 125 (VIS), Qr = 525×e-x/ 0.01467 + 195 (NIR), and Qr = 350×e-x/ 0.01663 + 285 (SWIR). (e) Variation in peak intensity of the upper branch as a function of Δn across the VIS, NIR, and SWIR spectral regions. (f) Comparison of peak sensitivity between conventional refractometric sensing and Q-switching sensing chips.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7176623/v1/6edee5de18b7e32dbaca1a7a.png"},{"id":87392342,"identity":"79aa8e1c-5311-4fc0-b15f-d8c38171b3c1","added_by":"auto","created_at":"2025-07-23 10:03:09","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":401848,"visible":true,"origin":"","legend":"\u003cp\u003eMiniaturization and characterization of Q-switching sensing system. (a) Schematic and AFM characterization of gradient SiO₂ bars with thicknesses of 10, 20, and 30 nm deposited on the Q-switching sensing chip. (b) Reconstructed morphologies of the gradient pattern acquired using a hyperspectral imaging system with a tunable narrowband laser (2 nm bandwidth, blue square) and broadband incoherent illumination (10 nm bandwidth, yellow square; 25 nm bandwidth, brown square). (c) Miniaturized Q-switching sensing device integrating an LED (cost ~1 USD), photodetector (PD, cost ~5 USD), and Q-switching sensor chip (cost ~5 USD/chip) into a compact cross-shaped system. (d) Wafer-scale fabrication of 3D BIC metasurfaces using AL-3Dlitho. (e) Simulation and experimental validation of defect tolerance in local and nonlocal modes; simulations vary DA (160 – 240 nm) with fixed DB (200 nm), and wafer-scale spectra confirm robust nonlocal mode performance. (f) Measured electrical signal intensity of the Q-switching sensing system under bulk refractive index sensing. (g) Real-time kinetic binding curves depicting the Protein A–IgG association and dissociation process.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7176623/v1/3890f5018a3718faaa708ead.png"},{"id":87393304,"identity":"7cec4a51-6467-4477-be88-3e53812f3f3f","added_by":"auto","created_at":"2025-07-23 10:11:09","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":309280,"visible":true,"origin":"","legend":"\u003cp\u003eClinical lung cancer (LC) diagnosis and postoperative monitoring using Q-switching sensing system. (a) Schematic overview of the clinical LC detection workflow employing the Q-switching sensing system. (b) Measured spectra indicate signal intensity responses from the Q-switching sensing chip during bio-functionalization and sEV binding. (c) Representative SEM images of the Q-switching sensing chip before and after sEV capture. (d) Real-time intensity signals recorded during sEV association using the Q-switching sensing system. (e) Comparison of signal responses from Q-switching sensing and ELISA across a range of sEV concentrations, fitted using a four-parameter logistic (4PL) model; limit of detection (LOD) determined via anti-CD63-mediated titration. (f) Q-switching sensing analysis of clinical serum samples targeting CD63 and CD151 markers from LC patients (n = 34) and healthy controls (n = 22). (g) Statistical distribution of intensity responses for LC and control groups using CD63 and CD151 markers. (h) Biomarker analysis in serum samples from LC patients (n = 28), comparing vesicle-associated targets measured by Q-switching sensing (top) and total protein levels measured by conventional ELISA (bottom). (i) Representative MRI scans before and after treatment from clinical responders and non-responders. (j) ROC curves are utilized to compare Q-switching sensing and ELISA regression models for LC serum sample classification. All measurements were performed in triplicate; data in (f) are presented as mean ± s.d.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7176623/v1/cbc5c22ef80033103557b195.png"},{"id":87393554,"identity":"ac8288a2-ebc1-4fa0-889d-d9b2e1944829","added_by":"auto","created_at":"2025-07-23 10:19:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2490282,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7176623/v1/c5268aaa-c13a-4faf-a3b7-926703d61e9c.pdf"},{"id":87392345,"identity":"ec93f6fb-f56e-41ab-9311-fec625efee0e","added_by":"auto","created_at":"2025-07-23 10:03:10","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":22250305,"visible":true,"origin":"","legend":"supplementary material for Q-Switching Nanophotonic Biosensing","description":"","filename":"supplementarymaterial2025finalnaturephotonics.docx","url":"https://assets-eu.researchsquare.com/files/rs-7176623/v1/f5595e059426d98312abb136.docx"}],"financialInterests":"\u003cb\u003eYes\u003c/b\u003e there is potential Competing Interest.\nL.Y.W., J.C.S., S.S.Y. and X.D.W. have a pending patent application related to the Q-switching nanophotonic biosensing used in this paper, described in a Chinese patent through Westlake University (Chinese patent no. CN 119376096 A) for technology related to Q-switching sensing physics, chip fabrication and its integrated sensing system. The other authors declare no competing interests.","formattedTitle":"Q-Switching Nanophotonic Biosensing","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDetecting biomarkers with high sensitivity is crucial for advancing medical diagnostics, particularly in enabling real-time disease monitoring without reliance on fluorescent or radioactive labels\u003csup\u003e1,2\u003c/sup\u003e. Nanophotonic biosensors have emerged as critical analytical tools, typically employing a single-oscillator that detects and responds separately to either the real\u003csup\u003e3-10\u003c/sup\u003e or imaginary\u003csup\u003e11-15\u003c/sup\u003e components of the refractive index (\u003cstrong\u003eFig. 1a\u003c/strong\u003e, first and third quadrants). Among them, refractometric biosensors uniquely operate in a label-free and real-time manner by translating subtle refractive index changes (\u003cem\u003e\u0026Delta;n\u003c/em\u003e) into resonance frequency shifts (\u003cem\u003e\u0026Delta;\u0026omega;\u003c/em\u003e), offering broad compatibility with diverse biomolecular interactions and thus making them widely regarded for probing biomarkers in both fundamental research and clinical application\u003csup\u003e16-22\u003c/sup\u003e. However, the inherently small \u003cem\u003e\u0026Delta;n\u003c/em\u003e at the nanoscale yields subtle spectral shifts, which limit signal clarity and require complex instrumentation, thereby constraining the scalability of these approaches\u003csup\u003e1,22\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eAlthough recent advances in nanophotonic devices have introduced non-radiative optical modes that hold promise for enhancing refractometric sensing by enabling narrowband resonances and strong light-matter interactions\u003csup\u003e23-29\u003c/sup\u003e, these approaches often involve suppressing radiative losses while maintaining fixed quality factors (\u003cem\u003eQ\u003c/em\u003e-factor) without energy radiation during sensing (\u003cstrong\u003eFig. 1b\u003c/strong\u003e). As a result, signal intensity is inevitably diminished with high \u003cem\u003eQ\u003c/em\u003e-factor, requiring increasingly sophisticated optical setups to detect minor spectral shifts (\u003cstrong\u003eFig. 1c\u003c/strong\u003e)\u003csup\u003e22,30,31\u003c/sup\u003e. Moreover, the precise and costly fabrication required for these fragile high quality resonators, combined with their sensitivity to structural defects, presents further barriers to scalable deployment\u003csup\u003e1,32,33\u003c/sup\u003e. Together, these challenges underscore the urgent need for a fundamentally new sensing strategy that can robustly amplify weak refractive index changes into clearly distinguishable, low-complexity optical outputs.\u003c/p\u003e\n\u003cp\u003eHere, we introduce a \u003cem\u003eQ\u003c/em\u003e-switching sensing mechanism based on coupled-oscillators that circumvents the intrinsic physical barrier among single-oscillator biosensors by directly linking the real part of the refractive index (\u003cem\u003e\u0026Delta;n\u003c/em\u003e) with the imaginary component of the optical frequency (\u003cem\u003e\u0026Delta;i\u0026gamma;\u003c/em\u003e). While \u003cem\u003eQ\u003c/em\u003e-switching is a well-established approach in laser physics, its biosensing analogue has remained largely unexplored. In this framework, biomarker binding induces a rapid \u003cem\u003eQ\u003c/em\u003e-factor switching, generating amplified, intensity-based signals (\u003cem\u003e\u0026Delta;I\u003c/em\u003e). The \u003cem\u003eQ-\u003c/em\u003eswitching sensing chip is implemented by a three-dimensional bound-state-in-the-continuum (3D BIC) metasurface that supports nonlocal, \u003cem\u003eQ\u003c/em\u003e-switchable modes within a strong coupling system. This architecture achieves a lattice-independent peak sensitivity exceeding 10\u003csup\u003e3\u003c/sup\u003e %/RIU across the visible, near-infrared, and short-wave infrared regimes in experiment, representing an order-of-magnitude enhancement over conventional refractometric biosensors. Fabricated using a wafer-scale, aluminum-based 3D lithography (AL-3Dlitho) process, the defect-tolerant nonlocal metasurface demonstrates high scalability across 8-inch substrates, with a resonance deviation of less than 0.97 nm. Integrated into a compact, diode-driven point-of-care testing (POCT) platform, the \u003cem\u003eQ\u003c/em\u003e-switching sensing system enables rapid and ultrasensitive detection of small extracellular vesicles (sEVs) down to 24 attomolar, corresponding to a 10⁴-fold improvement in sensitivity compared to commercial enzyme-linked immunosorbent assays (ELISA). In clinical validation, the diagnostic tests achieve a 93.5% area under the curve (AUC) of receiver operating characteristic (ROC) for postoperative lung cancer monitoring. By establishing a previously untapped sensing mechanism, this work establishes a versatile and scalable platform for next-generation, high-performance diagnostic technologies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eQ\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e-switching sensing mechanism\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCoupled-oscillators were introduced to unify single-oscillator sensing mechanisms across both the real and imaginary domains, thereby extending the operational regime into the second and fourth quadrants of the complex sensing space (\u003cstrong\u003eFig. 1a\u003c/strong\u003e). This study focuses on the third-quadrant regime, referred to as \u003cem\u003eQ\u003c/em\u003e-switching sensing. The intrinsic quality factor of the system, defined as \u003cem\u003eQ\u003c/em\u003e = (1/\u003cem\u003eQ\u003csub\u003er\u003c/sub\u003e\u003c/em\u003e + 1/\u003cem\u003eQ\u003csub\u003en\u003c/sub\u003e\u003c/em\u003e)\u003csup\u003e-1\u003c/sup\u003e, reflects the balance between radiative (1/\u003cem\u003eQ\u003csub\u003er\u003c/sub\u003e\u003c/em\u003e) and non-radiative (1/\u003cem\u003eQ\u003csub\u003en\u003c/sub\u003e\u003c/em\u003e) loss channels\u003csup\u003e34\u003c/sup\u003e. Here, \u003cem\u003eQ\u003csub\u003en\u003c/sub\u003e\u003c/em\u003e is determined by material absorption or intrinsic dissipation, while \u003cem\u003eQ\u003csub\u003er\u003c/sub\u003e\u003c/em\u003e is sensitive to external perturbations such as biomarker binding. During \u003cem\u003eQ\u003c/em\u003e-switching sensing, variations in the ambient refractive index predominantly affect \u003cem\u003eQ\u003csub\u003er\u003c/sub\u003e\u003c/em\u003e, with negligible influence on \u003cem\u003eQ\u003csub\u003en\u003c/sub\u003e\u003c/em\u003e. The dependence of \u003cem\u003eQ\u003csub\u003er\u003c/sub\u003e\u003c/em\u003e on \u003cem\u003e\u0026Delta;n\u003c/em\u003e in the strong coupling system is governed by the relation:\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDevelopment of 3D BIC metasurface for \u003cem\u003eQ\u003c/em\u003e-switching sensing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo optimize \u003cem\u003eQ\u003c/em\u003e-switching sensing performance, precise control over radiative channels in a strong coupling BIC system is essential. Conventionally, in-plane geometric asymmetry (\u003cem\u003e\u0026Delta;D\u003c/em\u003e \u0026gt; 0, \u003cem\u003e\u0026Delta;Z\u003c/em\u003e = 0) has been employed to modulate radiative energy leakage in a strong coupling system\u003csup\u003e36-38\u003c/sup\u003e. However, this approach induces crosstalk among multiple quasi-BIC (qBIC) resonances within the strong coupling system due to the geometry-sensitive nature of the modes (Supplementary Note S2\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eand\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eFig. S6), resulting in unstable resonance frequencies and fluctuating radiative energy behavior (\u003cstrong\u003eFig. 2a,\u0026nbsp;\u003c/strong\u003eSupplementary Note S3 and\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eFig. S7a). As an alternative, we introduce out-of-plane spatial asymmetry by vertically displacing the constituent nanoparticles (\u003cem\u003e\u0026Delta;D\u003c/em\u003e = 0, \u003cem\u003e\u0026Delta;Z\u003c/em\u003e \u0026gt; 0) (\u003cstrong\u003eFig. 2b\u003c/strong\u003e). This design preserves the dipole moment of the nanoparticles, stabilizes the resonance frequencies, and enables controllable \u003cem\u003eQ\u003c/em\u003e-factor tuning for both the upper (qBIC\u003csub\u003eU\u003c/sub\u003e) and lower (qBIC\u003csub\u003eL\u003c/sub\u003e) branches of the qBIC resonances. Extending this symmetry breaking into three dimensions, forming a \u0026ldquo;3D BIC metasurface\u0026rdquo;, provides a robust mechanism for modulating radiative losses in multimode, strong coupling (Supplementary Fig. S7b-d). More detailed optimization procedures and derivations are provided in Supplementary Note S3 and Figs. S8\u0026ndash;S13.\u003c/p\u003e\n\u003cp\u003eFor the fabrication of the 3D BIC metasurfaces, an AL-3Dlitho technique was employed, which enabled precise and scalable control of out-of-plane displacement\u003csup\u003e9,39,40\u003c/sup\u003e. This method firstly used binary-pore anodic aluminum oxide templates to construct 3D mother silicon nanostructures (\u003cstrong\u003eFig. 2c\u003c/strong\u003e), followed by a lift-off process to transfer deposited gold films on the silicon substrate, resulting in the final 3D BIC metasurfaces. Scanning electron microscopy (SEM) and atomic force microscopy (AFM) confirmed the successful realization of dual-layer gold nanoparticle arrays with a vertical displacement of 140 nm (\u003cstrong\u003eFig. 2d\u003c/strong\u003e). Detailed fabrication procedures and morphological characterizations are provided in the Materials and Methods section MM2 and Supplementary Figs. S14-S16.\u003c/p\u003e\n\u003cp\u003eOptical measurements (Materials and Methods section MM3 and\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eSupplementary Fig. S17) revealed hallmark features of qBIC-coupled resonances, where their linewidths transition from undetectable to broad as \u003cem\u003e\u0026Delta;Z\u003c/em\u003e increases from 0 to 140 nm (\u003cstrong\u003eFig. 2e\u003c/strong\u003e). These experimental observations closely match the numerical simulations. To account for fabrication-induced losses, the simulated spectra incorporate a 1.2-fold enhancement in the imaginary component of the gold refractive index, \u003cem\u003eIm(n\u003csub\u003eAu\u003c/sub\u003e)\u003c/em\u003e. Despite this adjustment, the qBIC\u003csub\u003eU\u0026nbsp;\u003c/sub\u003eexhibits minimal frequency shift, underscoring its intrinsic robustness to material loss (Supplementary Fig. S18). Deposition of TiO₂ film on the 3D metasurface with \u003cem\u003e\u0026Delta;Z\u003c/em\u003e = 140 nm further demonstrates a strong coupling behavior, with clear Rabi splitting evident in the reflectance spectra (\u003cstrong\u003eFig. 2f)\u003c/strong\u003e. The onset of a monotonic increase in qBIC\u003csub\u003eU\u003c/sub\u003e intensity at \u003cem\u003et\u003csub\u003eTiO₂\u003c/sub\u003e\u003c/em\u003e = 80 nm closely mirrors the simulated response.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePerformance characterization of \u003cem\u003eQ\u003c/em\u003e-switching sensing chip\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe \u003cem\u003eQ\u003c/em\u003e-switching sensing mechanism exhibits distinct behavior compared to conventional refractometric sensing, both in near-field and far-field characterization. As shown in \u003cstrong\u003eFig. 3a\u003c/strong\u003e, refractometric sensing relies on complete near-field overlap between the analyte and the optical mode, maximizing the mode overlap factor to induce a resonance shift\u003csup\u003e1,3\u003c/sup\u003e. However, such near-field localization usually leads to increased energy dissipation and a lower \u003cem\u003eQ\u003c/em\u003e factor\u003csup\u003e41,42\u003c/sup\u003e. \u003cem\u003eQ\u003c/em\u003e-switching sensing can achieve remote detection by spatially separating the analyte (in the superstrate) from the optical mode (in the substrate) during the sensing process. As a result, a high-\u003cem\u003eQ\u003c/em\u003e nonlocal mode can be efficiently switched by a \u003cem\u003e\u0026Delta;n\u003c/em\u003e, producing amplified energy fluctuations without requiring strong near-field\u0026nbsp;overlap with the analyte, thereby preserving its radiationless character and high sensitivity.\u003c/p\u003e\n\u003cp\u003eTo validate the universality of \u003cem\u003eQ\u003c/em\u003e-switching sensing, we conducted both theoretical simulation and experimental measurements on metasurfaces with periodicities of 470, 600, and 800 nm, assessing their far-field bulk refractive index sensitivity across the visible (VIS), near-infrared (NIR), and short-wavelength infrared (SWIR) spectral regions (\u003cstrong\u003eFig. 3b\u003c/strong\u003e and Supplementary Figs. S19\u0026ndash;S21). The frequency shift of the qBIC\u003csub\u003eL\u003c/sub\u003e, which follows conventional refractometric sensing principles, diminishes rapidly at shorter wavelengths due to smaller lattice constants (\u003cstrong\u003eFig. 3c\u003c/strong\u003e). Conversely, the initial \u003cem\u003eQ\u003csub\u003er\u003c/sub\u003e\u003c/em\u003e of the qBIC\u003csub\u003eU\u003c/sub\u003e increases at shorter wavelengths and undergoes faster switching toward the corresponding \u003cem\u003eQ\u003csub\u003en\u003c/sub\u003e\u003c/em\u003e (\u003cstrong\u003eFig. 3d\u003c/strong\u003e), resulting in a lattice-independent intensity response in \u003cem\u003eQ\u003c/em\u003e-switching sensing (\u003cstrong\u003eFig. 3e\u003c/strong\u003e). Experimentally extracted \u003cem\u003eQ\u003csub\u003en\u003c/sub\u003e\u003c/em\u003e values in the VIS, NIR, and SWIR bands are 58.6, 64.2, and 115, respectively, with detailed fitting parameters provided in Supplementary Fig. S20.\u003c/p\u003e\n\u003cp\u003eMetasurface-based refractometric biosensors are fundamentally limited not only by their lattice constants but also by their \u003cem\u003eQ\u0026nbsp;\u003c/em\u003efactors. Achieving a high \u003cem\u003eQ\u003c/em\u003e factor in conventional refractometric designs typically requires strong optical mode confinement within the resonator, which paradoxically reduces near-field overlap with the analyte and thereby limits sensitivity\u003csup\u003e22,43,44\u003c/sup\u003e. As shown in \u003cstrong\u003eFig. 3f\u003c/strong\u003e, the normalized peak sensitivity (\u003cem\u003eS\u003csub\u003e\u0026lambda;\u003c/sub\u003e\u003c/em\u003e/\u003cem\u003e\u0026lambda;\u003csub\u003e0\u003c/sub\u003e\u003c/em\u003e) of various metasurfaces operating with constant \u003cem\u003eQ\u003c/em\u003e factors decreases as the \u003cem\u003eQ\u003c/em\u003e factor increases, remaining below 10\u0026sup2; %/RIU\u003csup\u003e45-52\u003c/sup\u003e. In contrast, \u003cem\u003eQ\u003c/em\u003e-switching sensing chips\u0026nbsp;exhibit normalized peak sensitivities (\u003cem\u003eS\u003csub\u003eI\u003c/sub\u003e\u003c/em\u003e/\u003cem\u003eI\u003csub\u003e0\u003c/sub\u003e\u003c/em\u003e) on the order of ~10\u0026sup3; %/RIU across the VIS, NIR, and SWIR bands in experiment, representing an order-of-magnitude enhancement.\u0026nbsp;These findings underscore the unique capability of \u003cem\u003eQ\u003c/em\u003e-switching sensing to resolve the longstanding intrinsic trade-off between energy dissipation and sensitivity in frequency-shift-based single-oscillator biosensors, establishing it as a spectrally versatile approach for broadband biochemical detection.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMiniaturization and Scalability of \u003cem\u003eQ\u003c/em\u003e-switching sensing system\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConventional spectrometer-free biosensing systems based on high-\u003cem\u003eQ\u003c/em\u003e resonators typically demand narrowband, frequency-aligned light sources, imposing stringent requirements on the optical setup. These constraints lead to bulky, complex instruments, as minimal resonance mismatch often fail to generate sufficiently detectable signals\u003csup\u003e26,53,54\u003c/sup\u003e. \u003cem\u003eQ\u003c/em\u003e-switching sensing systems achieve superior signal responses and enhanced detectability while dramatically simplifying the optical configuration, requiring only an incoherent light source such as a broadband optical filter or LED.\u003c/p\u003e\n\u003cp\u003eTo demonstrate this advantage, gradient SiO\u003csub\u003e2\u003c/sub\u003e bars with thicknesses of 10, 20, and 30 nm were sequentially deposited onto the \u003cem\u003eQ\u003c/em\u003e-switching sensing chip using photolithographic overlay techniques (\u003cstrong\u003eFig. 4a\u003c/strong\u003e and Supplementary Fig. S22). The nanoscale morphology was reconstructed under both coherent narrowband illumination (2 nm bandwidth) and incoherent broadband illumination (10 and 25 nm bandwidths) (\u003cstrong\u003eFig. 4b\u003c/strong\u003e). Under narrowband conditions, slight wavelength deviations (in step of 6 nm) led to substantial reconstruction errors, highlighting the limitations of narrowband-resonance-based imaging (Supplementary Fig. S23). While reconstructions obtained using broadband filter and LED illumination closely matched the ground truth of the gradient pattern, demonstrating high accuracy and reliability. These results underscore the potential of \u003cem\u003eQ\u003c/em\u003e-switching sensing systems, which integrate an LED, photodetector, 3D BIC metasurface, and beam splitter into a handheld platform (\u003cstrong\u003eFig. 4c\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eFurthermore, the distinctive design of the 3D BIC metasurface allows for scalable fabrication via AL-3Dlitho on 6- and 8-inch wafers (\u003cstrong\u003eFig. 4d\u003c/strong\u003e). A key advantage of this system lies in the exceptional robustness of the nonlocal \u003cem\u003eQ\u003c/em\u003e-switchable mode against fabrication defects (\u003cstrong\u003eFig. 4e\u003c/strong\u003e). When a \u003cem\u003e\u0026Delta;D\u003c/em\u003e (\u003cem\u003eD\u003csub\u003eA\u003c/sub\u003e\u003c/em\u003e \u0026minus; \u003cem\u003eD\u003csub\u003eB\u003c/sub\u003e\u003c/em\u003e) variation is introduced between binary nanoparticles with a 600 nm period, the low-\u003cem\u003eQ\u003c/em\u003e local mode exhibits significant wavelength shifts in response to the defect. In contrast, the high-\u003cem\u003eQ\u003c/em\u003e nonlocal mode remains remarkably stable, maintaining a center wavelength near 940 nm with only a 0.97 nm deviation in wafer-scale samples. The combination of the \u003cem\u003eQ\u003c/em\u003e-switching sensing mechanism with a scalable 3D BIC metasurface (as low as 5 USD per chip) offers a highly integrated, cost-effective commercialized sensing system (Table S1).\u003c/p\u003e\n\u003cp\u003eDespite its compact footprint, the miniaturized system effectively translates \u003cem\u003eQ\u003c/em\u003e-switching optical responses into prominent electrical signal variations, achieving a limit of detection (LOD) of 1.1486 \u0026times; 10⁻⁵ RIU (\u003cstrong\u003eFig. 4f\u0026nbsp;\u003c/strong\u003eand\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eSupplementary Fig. S24). This performance rivals the leading capabilities of conventional high-\u003cem\u003eQ\u003c/em\u003e metasurfaces\u003csup\u003e55\u003c/sup\u003e and other miniaturized SPR biosensors\u003csup\u003e56\u003c/sup\u003e. To demonstrate its practical effectiveness, we tested different concentrations of IgG using the miniaturized \u003cem\u003eQ\u003c/em\u003e-switching sensing system, applying a 5-minute binding period followed by a 10-minute buffer wash. The calculated dissociation rate constant (K\u003csub\u003eD\u003c/sub\u003e) was 3.125 nM (\u003cstrong\u003eFig. 4g\u003c/strong\u003e). These findings confirm the potential of our system as a high-performance platform for the rapid and sensitive evaluation of biomolecular binding events.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical\u003c/strong\u003e \u003cstrong\u003eapplication of \u003cem\u003eQ\u003c/em\u003e-switching sensing\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;system\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo demonstrate the clinical translation potential of our \u003cem\u003eQ\u003c/em\u003e-switching POCT sensing system, we conducted a proof-of-concept study targeting lung cancer (LC), one of the leading causes of cancer-related mortality worldwide (\u003cstrong\u003eFig. 5a\u003c/strong\u003e)\u003csup\u003e57\u003c/sup\u003e. sEVs were selected as biomarkers due to their growing significance in liquid biopsy approaches\u003csup\u003e58,59\u003c/sup\u003e. LC cells actively secrete sEVs via multivesicular endosome\u0026ndash;plasma membrane fusion, and their presence has been confirmed in multiple biofluids\u003csup\u003e60\u003c/sup\u003e. Nanoparticle tracking analysis revealed a unimodal size distribution centered around ~120 nm (Supplementary Fig. S25). These sEVs encapsulate tumor-associated molecular cargos, including established markers such as CD63 and CD151, highlighting their diagnostic relevance.\u003c/p\u003e\n\u003cp\u003eTo interrogate sEV membrane proteomic profiles, we engineered \u003cem\u003eQ-\u003c/em\u003eswitching sensing chips with ligand-specific immobilization strategies (see Materials and Methods section MM4\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eand\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eSupplementary Fig. S26). Optical signal intensity increased markedly from the bare metasurface to sEVs binding, indicating strong and specific molecular recognition (\u003cstrong\u003eFig. 5b\u003c/strong\u003e and Supplementary Fig. S27). SEM imaging further confirmed the selective capture of sEVs on the CD63-functionalized metasurface (\u003cstrong\u003eFig. 5c\u003c/strong\u003e). Notably, our sensing system enabled rapid screening of native samples with varying sEV concentrations by monitoring real-time intensity changes within 15 minutes (\u003cstrong\u003eFig. 5d\u003c/strong\u003e). Quantitative analysis using four-parameter logistic regression established a detection limit of 72 sEVs (~24 aM), representing a remarkable 10⁴-fold sensitivity enhancement over the conventional ELISA (which require ~6 \u0026times; 10⁵ sEVs) (\u003cstrong\u003eFig. 5e\u0026nbsp;\u003c/strong\u003eand\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eSupplementary Fig. S28).\u003c/p\u003e\n\u003cp\u003eTo validate the clinical utility of our approach, we conducted a case-control study using serum samples from 34 lung cancer (LC) patients and 22 age-matched healthy donors (\u003cstrong\u003eFig. 5f\u003c/strong\u003e and Table S2). High-throughput \u003cem\u003eQ\u003c/em\u003e-switching sensing of CD63 and CD151 expressions in clinical samples revealed significant distinguishment between LC and healthy controls \u003cstrong\u003e(Fig. 5g\u003c/strong\u003e), demonstrating excellent diagnostic performance. To further assess prognostic potential, we analyzed serum from 16 favorable- and 12 poor-prognosis patients (\u003cstrong\u003eFig. 5h,\u003c/strong\u003e top). For comparison, conventional ELISA was performed to quantify total protein levels (\u003cstrong\u003eFig. 5h,\u0026nbsp;\u003c/strong\u003ebottom\u003cstrong\u003e,\u0026nbsp;\u003c/strong\u003eand\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eMaterials and Methods section MM4). Unlike ELISA, which lacks specificity for vesicle-bound markers, \u003cem\u003eQ\u003c/em\u003e-switching sensing directly targeted sEV-associated markers, revealing distinct expression patterns aligned with disease status, consistent with prior reports\u003csup\u003e61-64\u003c/sup\u003e. Magnetic resonance imaging (MRI) scans before and after treatment further validated these clinical outcomes, showing tumor regression and reduced ground-glass opacities in responders, while non-responders exhibited clear disease progression (\u003cstrong\u003eFig. 5i\u0026nbsp;\u003c/strong\u003eand Supplementary Fig.S29).\u003c/p\u003e\n\u003cp\u003eWe developed regression-based scoring models\u0026nbsp;from\u0026nbsp;\u003cem\u003eQ\u003c/em\u003e-switching\u0026nbsp;sensing\u0026nbsp;and ELISA data\u0026nbsp;to\u0026nbsp;evaluate their prognostic performance using\u0026nbsp;receiver operating characteristic analysis\u0026nbsp;(\u003cstrong\u003eFig. 5j\u003c/strong\u003e). Notably, the \u003cem\u003eQ\u003c/em\u003e-switching sensing significantly outperformed ELISA, achieving an\u0026nbsp;AUC of 93.2%, compared to 69.3%.\u0026nbsp;These findings underscore the superior analytical power of the sEV-targeted \u003cem\u003eQ\u003c/em\u003e-switching assay, which captures disease-relevant biological signals grounded in cellular origin and vesicle-mediated mechanisms. By enabling highly sensitive and precise detection of vesicle-derived biomarkers, the \u003cem\u003eQ\u003c/em\u003e-switching sensing offers strong potential for advancing early cancer diagnosis and longitudinal disease monitoring across multiple cancer types.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eWe\u0026nbsp;report\u0026nbsp;a \u003cem\u003eQ\u003c/em\u003e-switching sensing mechanism based on coupled-oscillators, which fundamentally overcomes the intrinsic limitations of conventional single-oscillator sensors by converting subtle refractive index variations into amplified optical intensity signals. This mechanism is realized via a 3D\u0026nbsp;BIC metasurface with engineered out-of-plane asymmetry, enabling robust excitation of nonlocal, \u003cem\u003eQ\u003c/em\u003e-switchable modes under strong coupling. The resulting \u003cem\u003eQ\u003c/em\u003e-switching sensing\u0026nbsp;chips\u0026nbsp;exhibit lattice-independent, broadband responses spanning the VIS to SWIR regimes and are readily integrated into compact, diode-driven systems. It achieves a refractive index detection limit of 10⁻⁵ RIU and enables ultrasensitive detection of small extracellular vesicles at concentrations as low as 24 aM,\u0026nbsp;a \u0026gt;10⁴-fold improvement over standard ELISA. In clinical studies, the system demonstrated a diagnostic AUC of 93.2%, highlighting its translational potential. Rather than suppressing radiative losses,\u0026nbsp;\u003cem\u003eQ\u003c/em\u003e-switching sensing harnesses them, facilitating loss-driven nanophotonic biosensing in complex space. This approach offers a highly sensitive, portable, and scalable solution for next-generation diagnostics in clinical, point-of-care, and resource-limited settings.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u003c/strong\u003e The authors thank the facility support and technical assistance from the Westlake Centre for Micro/Nano Fabrication, the Instrumentation and Service Centre for Physical Sciences (ISCPS), and the Instrumentation and Service Centre for Molecular Sciences (ISCMS) at Westlake University.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNatural Science Foundation of China (Grants No. 52373238, 52003225, 62175205, and U2130112)\u003c/p\u003e\n\u003cp\u003eResearch Centre for Industries of the Future at Westlake University (RCIF, Grant No. WU2022C024)\u003c/p\u003e\n\u003cp\u003eSpecial Support Plan for Photoelectric Chips Research at Westlake University (Grant No. 10300000H062201)\u003c/p\u003e\n\u003cp\u003eKey Project of Westlake Institute for Optoelectronics (Grant No. 2023GD005) and Westlake Education Foundation\u003c/p\u003e\n\u003cp\u003eThe Youth Talent Support Program of Fujian Province (Eyas Plan of Fujian Province) [2022]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions:\u003c/strong\u003e J.C.S. and L.Y.W. conceived the idea. J.F.Z. and L.Y.W. supervised the project. M.Q. provided helpful discussions. J.C.S. implemented the theoretical analysis, simulation, fabrication, and optical measurement. J.C.S. and J.H. built the measurement setup. F.J.L. and S.W.L. provided the bio-samples. X.D.W. and F.J.L. implemented the microfluidic biosensing test and data collection. J.C.S., F.J.L., X.D.W., S.S.Y., D.W.N., and L.W. performed the data processing and analysis. J.C.S. and L.Y.W. wrote the manuscript with input from J.F.Z., F.J.L., and M.Q. All authors contributed to the manuscript and approved the final version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e L.Y.W.,\u0026nbsp;J.C.S.,\u0026nbsp;S.S.Y. and X.D.W.\u0026nbsp;have a pending patent application related to the \u003cem\u003eQ\u003c/em\u003e-switching nanophotonic biosensing used in this paper, described in a Chinese patent through Westlake University (Chinese patent no. CN 119376096 A) for technology related to \u003cem\u003eQ\u003c/em\u003e-switching sensing physics, chip fabrication and its integrated sensing system. The other authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData and materials availability:\u003c/strong\u003e All data are available in the main text or the supplementary\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eH. Altug, S.-H. Oh, S. A. Maier, J. Homola, Advances and applications of nanophotonic biosensors. \u003cem\u003eNature Nanotechnology\u003c/em\u003e\u003cstrong\u003e15\u003c/strong\u003e, 5\u0026ndash;16 (2022).\u003c/li\u003e\n \u003cli\u003eA. V. Kabashin, V. G. Kravets, A. N. Grigorenko, Label-free optical biosensing: going beyond the limits. \u003cem\u003eChemical Sociaty Review\u003c/em\u003e\u003cstrong\u003e52\u003c/strong\u003e, 6554\u0026ndash;6585 (2023).\u003c/li\u003e\n \u003cli\u003eA. J. Haes, R. P. V. 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The refractive indices of SiO₂ and the quartz substrate were set to \u003cem\u003en\u003c/em\u003e = 1.5, TiO₂ to \u003cem\u003en\u003c/em\u003e = 2.2, and NOA83 to \u003cem\u003en\u003c/em\u003e = 1.56.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMM2: Fabrication and characterization of 3D BIC metasurfaces\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFabrication of the BP-AAO Template\u003c/strong\u003e: The preparation of the binary-porous anodic aluminum oxide (BP-AAO) template involved several key steps. First, a nickel film with periodic nanopillars (period \u003cem\u003eP\u003c/em\u003e = 800 nm) was used to imprint an electropolished aluminum foil under a pressure of 15 kN cm⁻² for 3 minutes, generating a nanodot array with 800 nm spacing on the aluminum surface. The imprinted foil was then anodized at 320 V in a mixed electrolyte solution (comprising 4 g citric acid, 200 mL ethylene glycol, 200 mL deionized water, and 10 mL of 0.1 wt% H₃PO₄) at 30 °C for 2 hours, forming a highly ordered array of primary nanopores (A-pores).\u003c/p\u003e\n\u003cp\u003eSubsequently, the A-pores were widened by immersing the aluminum foil in a 5 wt% H₃PO₄ solution for 2 hours. To protect the pore structure during further processing, a conformal TiO₂ layer was deposited via atomic layer deposition (ALD) using a D100-4882 system (Yaona Electronics) at 150 °C for 70 cycles. Each ALD cycle consisted of 0.5 s exposure to titanium isopropoxide (Ti precursor, C₁₂H₂₈O₄Ti), 8 s N₂ purge, 0.1 s H₂O pulse, and another 8 s N₂ purge.\u003c/p\u003e\n\u003cp\u003eThe top surface of the BP-AAO was then milled using an ion beam milling system (IM4000plus) to fully open the B-pores. A PMMA layer was spin-coated on the surface, and the unoxidized aluminum foil was removed using a mixed etchant containing 1.5 wt% CuCl₂ and 53.2 wt% HCl. After etching, the PMMA layer was dissolved in acetone. Finally, the template was immersed in 0.1 M NaOH solution for 30 minutes at room temperature to generate secondary nanopores (B-pores) at the fourfold junctions of the A-pores. The obtained BP-AAO template was transferred to a silicon wafer using water as the transfer medium.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFabrication of the 3D Nanostructure on Silicon\u003c/strong\u003e: The silicon wafer was first etched using an inductively coupled plasma (ICP) system (Leuven ICP, HAASRODE-E200A) for 20 seconds with a gas mixture of CF₄ and SF₆, under conditions of 20 °C and 8 mT VAT. Tilted ion beam milling was employed to remove the barrier layer at the base of the A-pores (Fig. S14a–c). A 10 nm thick Cr sacrificial layer was deposited via physical vapor deposition (PVD) (Fig. S14d), after which the BP-AAO template was peeled off, revealing a bilayer Cr particle structure (Fig. S14e).\u003c/p\u003e\n\u003cp\u003eA second ICP etch was then conducted using the same gas mixture, with a 24-second etch time, to form the final 3D silicon nanostructure (Fig. S14f). A 50 nm gold (Au) layer was deposited by PVD, followed by NOA83 coating, enabling lift-off of the 3D nanostructure and formation of the 3D bound-state-in-continuum (BIC) metasurface (Figs. S14g, S14h). Additional morphology and fabrication details are shown in Fig. S15 (pre-2nd etch) and Fig. S16a (post-etch). Fig. S16b displays the final gold-coated 3D BIC metasurface.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCharacterization Techniques:\u003c/strong\u003eThe nanoscale morphologies presented in \u003cstrong\u003eFig. 2c\u003c/strong\u003e and \u003cstrong\u003eFig. 2d\u003c/strong\u003e, Supplementary Fig. S15, and Fig. S16 were characterized using an analytical field-emission scanning electron microscope (FE-SEM, Zeiss Gemini 450). Atomic force microscopy (AFM, Bruker Dimension ICON) was employed to obtain the topographical image shown in \u003cstrong\u003eFig. 2d\u0026nbsp;\u003c/strong\u003ebottom. Microscopic patterns were analyzed using a metallographic microscope (CEWEI LW750LJT).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLithographic Overlay Method\u003c/strong\u003e: The lithographic overlay shown in \u003cstrong\u003eFig. 4a\u003c/strong\u003e was implemented using a semi-automated mask aligner (MA/BA6 Gen4, SUSS MicroTec). ARP5350 photoresist (Allresist GmbH) was used for overlay lithography.\u003c/p\u003e\n\u003cp\u003eFor extracellular vesicles characterization, standard high-speed centrifugation protocols were used to isolate and purify serum-derived small extracellular vesicles. Their morphology was examined using a transmission electron microscope (TEM, HT-7700, Hitachi, Japan) operated at 100 kV.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMM3: Optical setup\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo measure the reflectance spectra and acquire hyperspectral images of the 3D BIC metasurfaces, we developed a custom dual optical path system (Supplementary Fig. S17). The visible (VIS) path covers 400–1000 nm, while the near-infrared (NIR) path spans 1000–1700 nm. A broadband supercontinuum laser (SC-5, YSL Photonics) serves as the illumination source, delivering 470–2400 nm output with \u0026gt; –10 dBm/nm power spectral density and \u0026lt; 0.1 dB spectral power jitter.\u003c/p\u003e\n\u003cp\u003eIn both paths, a beam expander (25 mm and 150 mm focal length lenses) ensures uniform illumination. Reflective Köhler illumination is achieved using a 250 mm focal length lens pair (L1 and L2) in combination with a beam splitter. In the VIS path, hyperspectral imaging is realized via a laser line tunable filter (LLTF CONTRAST™, Photon etc.) based on volume holographic gratings, offering \u0026gt;OD6 out-of-band rejection and FWHM bandwidth of ~1.75 nm at 700 nm (up to 2.5 nm at 1000 nm).\u003c/p\u003e\n\u003cp\u003eA high-resolution VIS camera (Digital Sight 50M, Nikon; 3.76 μm pixel size, 85% peak quantum efficiency) and a fiber spectrometer (TREX, TAIZI Technology; 200–1000 nm) capture reflectance data. The NIR path employs a semiconductor-cooled camera (SWIR1300KMA, ToupTek; 1.31 MP, 400–1700 nm) and a high-resolution spectrometer (AQ6370D, YOKOGAWA; 0.02 nm resolution, 600–1700 nm, +20 to –90 dBm detection range) for precision resonance detection.\u003c/p\u003e\n\u003cp\u003eTo streamline functionality, a shared optical cage system—co-developed with Ray Cage (Zhenjiang) Photoelectric Technology Co. Ltd—accommodates both VIS and NIR paths on a common objective stage. This configuration supports simultaneous reflectance/transmission measurements and hyperspectral imaging in both domains, with illumination and detection possible from either side of the sample, significantly enhancing system versatility.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMM4: Biofunctionalization and serum detection analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSurface functionalization and biosensing Assay:\u0026nbsp;\u003c/strong\u003eThe \u003cem\u003eQ-\u003c/em\u003eswitching sensing chips were functionalized via covalent surface chemistry using (3-glycidoxypropyl) trimethoxysilane (3-GPS, Sigma-Aldrich, USA) for antibody immobilization. Cleaned metasurfaces were incubated in 1% (v/v) 3-GPS in toluene for 20 minutes, rinsed in fresh toluene, dried under nitrogen, and baked at 120 °C for 30 minutes to form a uniform silane monolayer on the silicon oxide surface. The terminal epoxide group of 3-GPS covalently binds to the amine groups of antibodies. CD151 capture antibodies (Thermo Fisher, USA) were immobilized directly onto the epoxy-silane-coated surfaces. To block nonspecific binding, the functionalized metasurfaces were incubated in 50 μg/mL bovine serum albumin (BSA, Sangon Biotech, China) for 30 minutes, followed by two PBS washes (5 min each). The resulting antibody-functionalized metasurfaces were then used to detect serum-derived extracellular vesicle (EV) membrane proteins.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eELISA benchmarking assay:\u0026nbsp;\u003c/strong\u003eA standard sandwich ELISA was conducted for comparison. Capture antibodies against CD63 and CD151 were immobilized on 96-well plates (SenBeiJia Biological) and blocked with 1% BSA. Clinical serum samples were thawed on ice, fixed with 4% paraformaldehyde, and permeabilized using 0.1% Triton X-100 for 15 min at room temperature. After treatment, 50 μL of each sample was added to the functionalized plates and incubated overnight at 4 °C. Plates were washed three times with PBS containing 0.05% Tween-20, incubated with HRP-conjugated secondary antibodies, and developed using a chemiluminescent substrate. Signal intensity was quantified using a microplate reader (Tecan).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Analysis and Ethics:\u0026nbsp;\u003c/strong\u003eAll measurements were performed in triplicate, and results are reported as mean ± standard deviation (s.d.). The number of replicates (N) is indicated in each statistical analysis. Significance between groups was assessed using a two-tailed Student’s t-test, with adjusted P-values \u0026lt; 0.001 considered statistically significant. Data analysis was performed using Origin software.\u003c/p\u003e\n\u003cp\u003eAll procedures involving human samples were approved by the Ethics Committee of the First Affiliated Hospital of Xiamen University (approval number: XMFHIIT-2024SL017). Written informed consent was obtained from all participants. Clinical sample collection and analysis adhered to the principles of the Declaration of Helsinki.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7176623/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7176623/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSensitive, label-free detection of biomarkers is critical for clinical diagnostics. However, conventional nanophotonic biosensors, typically based on single-oscillator architectures, remain confined to either real or imaginary sensing domain. This isolation often results in weak signal responses, limited operational stability, and high instrumental complexity. We introduce a \u003cem\u003eQ\u003c/em\u003e-switching sensing mechanism based on strongly coupled-oscillators that bridges the real and imaginary domains of nanophotonic biosensing. This mechanism amplifies subtle variations in the real part of the refractive index into pronounced switching of the radiative quality factor, enabling robust, intensity-based signal readout. The \u003cem\u003eQ\u003c/em\u003e-switching sensing chip is implemented in a defect-tolerant, nonlocal three-dimensional bound-state-in-the-continuum metasurface, fabricated via aluminum-based lithography on 8-inch wafers. As a result, it achieves lattice-independent peak sensitivity exceeding 10\u003csup\u003e3\u003c/sup\u003e %/RIU across the visible, near-infrared, and short-wave infrared regimes, an order of magnitude improvement over conventional refractometric biosensors. Integrated into a point-of-care testing system, this handheld, diode-driven \u003cem\u003eQ\u003c/em\u003e-switching sensing platform enables rapid detection of small extracellular vesicles at concentrations as low as 24 attomolar, offering a 10\u003csup\u003e4\u003c/sup\u003e-fold sensitivity enhancement over the mainstream ELISA for postoperative lung cancer monitoring. Grounded in \u003cem\u003eQ\u003c/em\u003e-switching physics, this strategy offers a scalable, high-performance biosensing platform for portable diagnostics in clinical, remote, and at-home settings.\u003c/p\u003e","manuscriptTitle":"Q-Switching Nanophotonic Biosensing","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-23 10:03:05","doi":"10.21203/rs.3.rs-7176623/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-photonics","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"nphoton","sideBox":"Learn more about [Nature Photonics](https://www.nature.com/nphoton/)","snPcode":"41566","submissionUrl":"https://mts-nphot.nature.com/cgi-bin/main.plex","title":"Nature Photonics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Research","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"5a310a4f-7b06-4ff2-bd4e-98872f3db1c5","owner":[],"postedDate":"July 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":51898478,"name":"Physical sciences/Optics and photonics/Applied optics/Optical sensors"},{"id":51898479,"name":"Physical sciences/Optics and photonics/Optical physics/Nanophotonics and plasmonics"},{"id":51898480,"name":"Physical sciences/Nanoscience and technology/Nanoscale devices/Nanophotonics and plasmonics"},{"id":51898481,"name":"Physical sciences/Optics and photonics/Optical materials and structures/Metamaterials"},{"id":51898482,"name":"Physical sciences/Optics and photonics/Applied optics/Optoelectronic devices and components"}],"tags":[],"updatedAt":"2026-04-03T04:00:57+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-23 10:03:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7176623","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7176623","identity":"rs-7176623","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}