Development of an Optical Biosensor Based on the Goos-Hänchen Shift and Surface Plasmon Resonance for Rapid Detection of Cancer Cells

Development of an Optical Biosensor Based on the Goos-Hänchen Shift and Surface Plasmon Resonance for Rapid Detection of Cancer Cells | 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 Research Article Development of an Optical Biosensor Based on the Goos-Hänchen Shift and Surface Plasmon Resonance for Rapid Detection of Cancer Cells Majid Karimi, Ebrahim Safari, Reza Safaralizadeh, Gholamreza Dehghan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6737507/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 26 Jul, 2025 Read the published version in Plasmonics → Version 1 posted 9 You are reading this latest preprint version Abstract Early detection of cancer cells is crucial for effective disease management and personalized treatment. This study presents an advanced optical biosensor that integrates the Goos-Hänchen (GH) shift with Surface Plasmon Resonance (SPR) for highly sensitive, label-free and rapid cancer cell detection. The system consists of a red diode laser, beam splitter, polarizer, high-refractive index rotatable prism, and quadrant detector (QD) for precise lateral beam shift measurements. A differential configuration with control and test targets minimizes noise and enhances measurement accuracy. Lung (A549) and colon (LS180) cancer cells were cultured on nanoscale gold-coated glass substrates, interacting with the evanescent wave under total internal reflection (TIR). SPR analysis revealed resonance dip shifts of ~ 1.6° for LS180 and ~ 2.2° for A549 cells, while GH shift measurements further improved diagnostic precision, yielding lateral displacements of ~ 5.8 µm for LS180 and ~ 6.5 µm for A549. The sensor has a detection limit of ~ 500,000 cells/cm² and a refractive index sensitivity of 160°/RIU for LS180 and 220°/RIU for A549. With a limit of detection (LOD) of ~ 6.8 × 10⁻⁴ RIU and a figure of merit (FOM) of 106.7 (LS180) and 146.7 (A549), the system demonstrated high resolution and sensitivity. The dynamic range spans refractive indices from ~ 1.33 to 1.37, enabling broad analyte detection. A signal-to-noise ratio (SNR) of 20:1 confirms robust signal reliability. By integrating the GH shift with SPR, this dual-mode biosensor significantly enhances sensitivity and accuracy, enabling rapid, non-invasive cancer cell detection. Its ability to distinguish between lung and colon cancer cells marks a valuable advancement in clinical diagnostics, supporting early detection and personalized medicine. Optical Biosensor Cancer Rapid Detection Laser Nanostructures Goos-Hänchen Shift Surface Plasmon Resonance Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Cancer is a leading cause of morbidity and mortality globally, with lung and colorectal cancers being among the most prevalent and deadly forms. According to the World Health Organization, lung cancer accounts for approximately 12.4% of all cancer diagnoses and 18.7% of cancer-related deaths, while colorectal cancer contributes to 9.6% of new cancer cases and 9.3% of cancer deaths worldwide [ 1 ]. These statistics underscore the urgent need for advanced diagnostic technologies capable of enabling rapid, accurate, and non-invasive detection of cancer cells. Early diagnosis significantly improves patient outcomes by facilitating timely therapeutic interventions, ultimately increasing survival rates and enhancing quality of life [ 2 ]. Traditional diagnostic techniques for cancer detection, including tissue biopsy, histopathological analysis, and molecular imaging, are often invasive, time-consuming, and expensive [ 3 , 4 ]. Furthermore, these methods often lack the sensitivity and specificity needed for early-stage diagnosis, leading to false positives/negatives and delays in treatment [ 5 , 6 ]. This highlights the growing demand for innovative, highly sensitive, and rapid diagnostic tools capable of detecting cancer cells in a label-free manner, preserving the natural state of biological samples [ 7 ]. Optical biosensors have gained significant attention as powerful diagnostic tools due to their high sensitivity, real-time monitoring, non-invasive operation and label-free detection capabilities. These sensors detect minute changes in optical properties, such as refractive index variations induced by biomolecule binding [ 8 , 9 ]. Among various optical sensing techniques, Surface Plasmon Resonance (SPR) has emerged as a widely adopted approach for detecting biomolecular interactions with high sensitivity and specificity. SPR utilizes the excitation of surface plasmons collective oscillations of free electrons at the metal-dielectric interface under total internal reflection (TIR) to generate an evanescent wave that penetrates a few hundred nanometers into the adjacent dielectric medium, making it highly sensitive to changes near the sensor surface [ 10 – 13 ]. Other optical techniques, such as optical coherence tomography (OCT), surface-enhanced raman spectroscopy (SERS), and reflectometric interference spectroscopy, also offer promising solutions for early cancer detection [ 8 , 14 ]. These advancements position optical biosensors as robust platforms for early disease diagnostics, offering advantages like reusability and ultrafast sensing capabilities [ 15 , 16 ]. SPR biosensors direct polarized light from a laser source towards a high-refractive-index prism coated with noble metal, typically gold, known for its excellent plasmonic properties and chemical stability. When the incident light satisfies the resonance condition, energy is transferred to the surface plasmons, resulting in a dip in the reflected light intensity at a specific angle, known as the SPR angle. A variation in the local refractive index such as the binding of cancer cells to the gold surface shifts the SPR angle, providing a sensitive and label-free means of detection. SPR has been extensively utilized for detecting a wide range of biological targets, including proteins, nucleic acids, and cancer biomarkers [ 11 , 17 – 19 ]. However, traditional SPR techniques are limited by their reliance on intensity or angular shifts alone, which may be influenced by environmental noise and systematic errors. This study presents a novel powerful optical biosensor combining GH Shift and SPR for accurate, rapid, sensitive, and label-free cancer cell detection. The GH shift refers to the lateral displacement of the reflected light beam at the interface between two media under TIR conditions [ 20 , 21 ]. It is extremely sensitive to changes in the refractive index at the sensor surface, making it ideal for detecting subtle changes induced by cancer cell binding or other biomolecules [ 21 , 22 ]. By combining the refractive index changes detected by SPR with the lateral displacement measured by the GH shift, the biosensor enhances detection sensitivity, providing additional information to improve overall accuracy [ 22 , 23 ]. By effectively distinguishing between lung and colon cancer cells, this system demonstrates significant potential for clinical cancer diagnostics and personalized medicine, paving the way for advancements in biomedical optics and cancer detection technologies. 2. Materials and methods 2.1. Materials 2.1.1. Optical Components A red diode laser (wavelength ≈ 633 nm, power ≈ 50 mW) was used as the coherent light source, providing a stable and monochromatic beam essential for precise SPR and GH shift measurements. A non-polarizing beam splitter (50:50 ratio) divided the incident laser beam into two identical optical paths, enabling simultaneous measurement on both control and test targets for differential analysis. A linear polarizer, placed after the laser, transmitted p-polarized light, optimizing SPR excitation by eliminating s-polarized components. A high-refractive-index BK7 glass prism (refractive index = 1.515 at λ = 633 nm) was used to achieve total internal reflection (TIR), generating the evanescent wave necessary for SPR excitation. The prism was mounted on a precision rotational stage with a resolution of ≈ 0.016°, allowing accurate angular adjustments. A high-sensitivity quadrant detector (QD) was used to measure lateral beam shifts with micrometer precision, essential for quantifying GH shift variations. The QD was interfaced with a data acquisition system for real-time voltage readouts, recording four distinct voltage outputs corresponding to the four quadrants of the detector, enabling the calculation of lateral shifts. 2.1.1 Substrate Preparation High-purity 24-karat gold (99.99% purity) was deposited onto glass slides (thickness = 1 mm) using electron beam evaporation (Fig. 1 a) under high vacuum conditions (10⁻⁶ Torr) to achieve a uniform nanoscale coating (≈ 50 nm). Using high-purity gold at the nanoscale ensures optimal SPR excitation due to the metal's superior plasmonic properties, chemical inertness, and biocompatibility. Gold's high electron density and ability to support surface plasmon polaritons make it an ideal choice for enhancing sensitivity in optical biosensing applications [ 11 , 14 , 24 ]. The thickness was monitored using a quartz crystal microbalance to ensure consistency across all samples. After deposition, the gold-coated substrates were annealed in a furnace under an argon atmosphere to improve the gold film's quality and uniformity. Annealing was performed at 375°C for 4 hours, with continuous argon flow at 100 mL/min. The annealing process optimized the gold layer's stability, crystallinity, and surface morphology, ensuring it adhered well to the glass and prevented detachment during subsequent cell culturing. The morphology and thickness of the gold layer were also characterized using optical microscopy imaging (Fig. 1 b) and Atomic force microscopy (AFM). 2.1.3. Cancer Cell Lines and Culture Conditions Human lung adenocarcinoma (A549) [ 25 , 26 ] and human colon adenocarcinoma (LS180) [ 27 , 28 ] cell lines were selected for their clinical relevance in cancer diagnostics and their distinct biological characteristics. These cells were cultured on the gold-coated substrates to induce localized refractive index changes upon interaction with the evanescent wave generated under TIR conditions [ 28 ]. Cells were cultured in Dulbecco’s Modified Eagle Medium (DMEM) supplemented with 10% fetal bovine serum (FBS), 1% penicillin-streptomycin, and 1% L-glutamine, all sourced from Sigma-Aldrich. The cells were maintained at 37°C in a humidified atmosphere with 5% CO₂. Gold-coated substrates were sterilized with 70% ethanol, followed by rinsing with phosphate-buffered saline (PBS, pH 7.4). Cells were seeded at a density of 5×10⁵ cells/cm² and allowed to adhere for 24 hours. For control measurements, substrates without cancer cells were prepared under identical conditions. 2.1.4. Reagents and Chemicals Phosphate-buffered saline (PBS) Used for washing and maintaining isotonic conditions during cell seeding and measurement procedures. Ethanol (70%) Utilized for substrate sterilization to prevent contamination. The reagents and chemicals were purchased from Merck or Sigma-Aldrich, ensuring high purity and consistency. 2.2. Experimental Setup and Procedure 2.2.1 Optical Configuration The red diode laser beam was directed toward a beam splitter, producing two identical beams. One beam was guided to the control target (bare gold-coated substrate), while the other was directed to the test target (gold-coated substrate with cancer cells). After the beam splitter, each beam passed through a polarizer to ensure p-polarization, which is essential for SPR excitation. The beams then passed through a high-refractive-index prism, rotated by a precision rotational stage. The angle of incidence (θ) was varied between 30° and 80° in 0.016° increments. Reflected beams from the gold interface were detected by quadrant detectors positioned at fixed distances from the prism. Voltage outputs from the four quadrants were recorded at each incident angle using a digital voltmeter (Fig. 4 ). A key feature of this setup is the use of a beam splitter to generate two identical optical paths: one directed at a control target with a bare gold-coated substrate and the other at a test target with cancer cells cultured on the gold surface. This differential configuration helps minimize environmental noise and systematic errors by providing a real-time reference, enabling precise detection of GH shift variations caused by the presence of cancer cells. 2.2.2. SPR Measurement SPR was excited when p-polarized light underwent total internal reflection (TIR) at the gold-dielectric interface, generating an evanescent wave sensitive to refractive index changes. By rotating the prism and recording the voltage outputs at each incident angle, SPR curves were obtained for both control and test targets. The reflected intensity was measured as a function of the incident angle, yielding the SPR curves. SPR dip positions were recorded for both control and test targets, allowing the detection of cancer cells based on local refractive index changes. The shift in SPR angle (Δθₛₚ r ) between control and test samples was calculated to quantify the cancer cell-induced refractive index changes. 2.2.3 GH Shift Measurement GH shift measurements provided complementary information by detecting lateral displacements of the reflected beam due to phase changes during TIR. This dual-mode sensing approach enhances the system's sensitivity and specificity and allows for precise differentiation between different cancer cells, facilitating rapid detection without the need for complex labeling procedures. Lateral shifts were quantified by analyzing the differential voltage outputs from the quadrant detectors, using the following equation [ 29 ]: Δx = k × (V Q1 ​+V Q4 ​−V Q2 ​−V Q3 ​) (1) Where V Q1 ​ to V Q4 ​ are the voltages from the four quadrants, and k is a calibration constant determined experimentally. By comparing GH shifts between control and test targets, cancer cell-induced refractive index changes were isolated, enhancing sensitivity and reducing environmental noise. 2.2.4 Validation and Reproducibility Experiments were repeated five times under identical conditions to ensure consistency and reproducibility of the results. 2.2.5 Data Analysis and Statistical Methods SPR curves were analyzed using Lorentzian fitting [ 30 , 31 ], while GH shift data were processed using Gaussian fitting [ 32 , 33 ]. Data were analyzed using one-way ANOVA, followed by Tukey’s post-hoc test for multiple comparisons. A p-value of < 0.05 was considered statistically significant [ 34 , 35 ]. Key performance metrics for the biosensor, including sensitivity, full width at half minimum (FWHM), figure of merit (FOM), dynamic range, Limit of Detection (LOD), and signal-to-noise ratio (SNR), were evaluated. These parameters are critical for assessing the sensor's ability to detect small changes in refractive index, distinguish between different cell types, and operate effectively across a wide range of analyte concentrations [ 36 – 39 ]. 3. Results and Discussion 3.1. Cell Adhesion and Morphology Figure 2 a shows the bare gold-coated samples fresh from the evaporation system, showing their general appearance. To confirm the successful attachment and uniform distribution of cancer cells on the gold-coated glass substrates, we first performed optical microscopy imaging. Figure 2 b and Fig. 2 c, demonstrate that both A549 (lung cancer) and LS180 (colon cancer) cells exhibited good adhesion and homogeneous distribution across the surface. The cell morphology appeared intact, indicating that the gold surface supported cellular attachment without inducing significant cytotoxicity. The formation of dense cellular layers suggests substantial alteration of the refractive index at the interface, which is critical for both SPR sensing and GH shift measurements. 3.2. AFM Imaging and Surface Morphology Figure 3 presents AFM topographic images (8.7 µm × 8.7 µm scale), illustrating the surface morphology of the samples. The images provide key qualitative insights into the sensor surface before and after cell adhesion. Figure 3 a depicts the bare gold-coated sample, revealing a typical granular topography associated with evaporated gold films. The height range of the surface varied from − 4.41 nm to 4.86 nm, indicating that the gold film had a relatively smooth surface with low roughness, which is essential for cellular adhesion studies and minimizing interference in biosensing applications. Upon A549 cell attachment (Fig. 3 b), the AFM analysis revealed a height range from − 56.9 nm to 61.8 nm, with a polynormalfit of 119 nm. This significant height variation suggests that A549 cells are more spread out on the surface due to strong cell-substrate adhesion. This morphology likely increases the interaction area with the evanescent wave in both SPR and GH shift measurements, thereby enhancing the sensor’s sensitivity. For the LS180 (Fig. 3 c), the AFM analysis showed a height variation of -11.5 nm to 8.44 nm, with a polynormalfit of 20 nm. These cells exhibited a more compact morphology on the gold surface compared to A549 cells, leading to a smaller contact area. This compact morphology likely results in reduced shifts in both SPR and GH measurements compared to A549 cells. The AFM results underscore the significant differences in cell morphology between A549 and LS180 cells, which are likely to affect their interactions with the optical biosensor. The larger, spread-out morphology of A549 cells suggests that they interact more extensively with the sensor surface, resulting in larger optical shifts, while the compact morphology of LS180 cells likely leads to reduced interactions, contributing to smaller shifts. This variation in cell morphology is an important factor in understanding the sensitivity and performance of optical biosensors for cancer cell detection, as cell shape and surface interactions directly influence the sensor's optical response. 3.3 SPR Measurements Figure 4 illustrates the experimental setup used for SPR detection, including a red diode laser, polarizer, beam splitter, high-refractive-index prism, quadrant detector and voltmeter. A schematic of this setup is shown in Fig. 5 . The obtained SPR reflectivity curves for the control (bare gold-coated substrate) and test samples (A549 and LS180 cancer cells) are presented in Fig. 6 . The bare gold-coated substrate exhibited an SPR dip at approximately 45.2°, indicating resonance at the gold-dielectric interface. Upon culturing A549 cells, a significant red shift of 2.2° was recorded, moving the resonance angle to about 47.4°. For LS180 cells, a red shift of 1.6° was observed, with the new resonance angle at approximately 46.8°. These shifts demonstrate that cancer cell adhesion altered the local refractive index, which modified the plasmonic resonance conditions at the metal-dielectric interface. The larger shift for A549 cells suggests a higher refractive index contribution, likely due to differences in cell composition, adhesion properties, and intracellular density. SPR measurements were repeated five times under identical conditions, yielding mean resonance shifts of 2.2° ± 0.03° for A549 cells and 1.6° ± 0.03° for LS180 cells. The low standard deviations indicate minimal variability, demonstrating the high precision of the system. SPR experiments with non-cancerous epithelial cells (negative control) showed negligible resonance shifts, confirming that the observed shifts were due to cancer-specific interactions. 3.4 GH Shift Measurements The GH shift, which quantifies the lateral displacement of the reflected beam, complements SPR data by detecting phase changes during TIR. For the bare gold-coated control sample, the GH shift was negligible (< 1 µm), as expected in the absence of refractive index changes. However, in the presence of cancer cells, a significant increase in lateral displacement was recorded. The GH shifts for A549 and LS180 cells were 6.5 ± 0.1 µm and 5.8 ± 0.1 µm, respectively. These shifts correlate well with the SPR dip shifts, demonstrating that GH shift measurements provide an additional layer of sensitivity to subtle changes in refractive index, complementing the SPR response. The correlation between SPR and GH shifts is summarized in Table 1 . Our biosensor successfully detected 500,000 cells/cm², aligning with or surpassing previous SPR-based cancer detection methods. Table 1 Summary of SPR and GH Shift Biosensor Results for Cancer Cell Detection. Parameter Value Description SPR Resonance Angle Shift for A549 2.2° Shift from bare substrate resonance angle. SPR Resonance Angle Shift for LS180 1.6° Shift from bare substrate resonance angle. GH Shift for A549 Cancer Cells 6.5 µm Lateral displacement of the reflected beam for A549. GH Shift for LS180 Cancer Cells 5.8 µm Lateral displacement of the reflected beam for LS180. Detection Limit (Cell Density) 500,000 cells/cm² Minimum cell density detectable by the sensor. Sensitivity - A549 Cancer Cells 220°/RIU Calculated sensitivity for A549 detection. Sensitivity - LS180 Cancer Cells 160°/RIU Calculated sensitivity for LS180 detection. FWHM – Both A549 and LS180 1.5° Full Width at Half Minimum of SPR curve for both cancer cell types. Figure of Merit (FOM) - LS180 106.7 A comprehensive measure of sensor performance for LS180 cells. Figure of Merit (FOM) - A549 146.7 A comprehensive measure of sensor performance for A549 cells. Limit of Detection (LOD) 6.8 × 10⁻⁵ RIU Minimum detectable refractive index change. Minimum Reflectivity for all samples 0.1 R min value observed across all conditions Stability 95% Percentage of data points within two standard deviations of the mean Dynamic Range 1.33–1.37 RIU Range of refractive indices detectable by the sensor. Signal-to-Noise Ratio (SNR) 20:1 The ratio of signal amplitude to noise amplitude, indicating signal quality. 3.5 Assessment of Key Analytical Parameters The sensitivity (S) of our biosensor, defined as the ratio of the shift in the SPR angle to the change in refractive index (S = Δθ SPR /Δn (2)) [ 40 , 41 ], was calculated to be 160°/RIU for LS180 and 220°/RIU for A549. In this work, Δn was assumed to be 0.01 RIU, based on typical values reported for biomolecular interactions in similar SPR experiments [ 42 – 44 ]. These values indicate that the biosensor is capable of detecting small refractive index changes caused by the presence of cancer cells, as summarized in Table 1 . The full width at half minimum (FWHM) assesses the resolution of the SPR sensor, where a smaller FWHM indicates better resolution [ 45 ]. We calculated the FWHM from the SPR curves and found it to be approximately 1.5° for both LS180 and A549 cells. The figure of merit (FOM) is a comprehensive measure of a sensor's performance, calculated as the ratio of sensitivity to the FWHM of the SPR reflectance dip: (FOM = S/FWHM (3)). FOM combines sensitivity and resolution, providing a holistic assessment of sensor performance. A higher FOM indicates better performance, reflecting both high sensitivity and a narrow resonance width [ 46 – 48 ]. For our biosensor, the FOM was calculated to be around 106.7 for LS180 cells and 146.7 for A549 cells. These values are also reported in Table 1 . The Limit of Detection (LOD) in sensors refers to the smallest change in refractive index that can be reliably detected. It is a critical parameter for evaluating SPR sensor performance, indicating the effectiveness in detecting small refractive index changes [ 49 – 52 ]. The LOD is typically determined by the background noise level and is defined as three times the standard deviation (3σ) of the blank noise, divided by the sensitivity of the system: (LOD = 3σ/S (4)) [ 53 – 55 ]. The calculated LOD for our biosensor was approximately 6.8 × 10⁻⁴ RIU, demonstrating the sensor’s ability to detect minimal refractive index changes Table 1 . Minimum Reflectivity (R min ) is a crucial parameter in surface plasmon resonance measurements, representing the lowest reflectivity value observed at the resonance angle [ 56 ]. This minimum occurs when the conditions for surface plasmon excitation are met, leading to a significant drop in the reflected light intensity. The position of this minimum reflectivity is sensitive to changes in the refractive index of the medium adjacent to the metal film, making it an essential indicator for detecting molecular interactions. The shift in the angle corresponding to minimum reflectivity (R min ) can be used to quantify changes in the local refractive index caused by analyte binding [ 18 ]. This allows for sensitive detection of biomolecular interactions. A sharper minimum reflectivity indicates better sensor performance, as it enhances sensitivity and resolution. The steepness of the reflectivity curve around R min is inversely related to the FWHM, which further influences detection accuracy. Achieving a low R min is critical for optimizing SPR sensor designs. Factors such as metal film thickness and material choice significantly impact R min , with optimal configurations resulting in sharper and deeper minima. The resonance angle (θ res ) corresponding to minimum reflectivity (R min ) can be determined from the reflectivity vs. angle of incidence curve (R(θ)). The relationship can be expressed as: R(θ)=∣r p ∣ 2 (5) where r p is the reflection coefficient for p-polarized light and is given by: r p = \(\:\frac{{\epsilon\:}2\text{k}1-{\epsilon\:}1\text{k}2}{{\epsilon\:}2\text{k}1+{\epsilon\:}1\text{k}2}\) (6) Where ε 1 and ε 2 are the dielectric constants of the two media and k 1 and k 2 are the z-components of the wave vectors in each medium. In our experiments, the R min values remained consistent across all conditions, with a value of approximately 0.1 (10%) for the bare gold-coated substrate, as well as for both A549 lung and LS180 colon cancer cells. The sharpness of the dips in the reflectivity curves further confirms the high resolution of the SPR sensor, which is crucial for detecting subtle refractive index changes induced by analyte binding. Stability refers to the ability of an SPR sensor to maintain consistent performance over time and under varying environmental conditions. It is crucial for ensuring reliable and accurate measurements, especially in long-term applications. Stability ensures that the sensor provides consistent results over extended periods, which is vital for monitoring biomolecular interactions or detecting analytes in real-time [ 57 – 59 ]. A stable sensor can withstand changes in temperature, humidity, and other environmental factors without compromising its performance. Using protection layers like self-assembled monolayers (SAMs) can enhance stability by preventing metal oxidation and degradation [ 60 ]. Choosing materials with inherent stability, such as gold, is common due to its resistance to oxidation compared to silver [ 58 ]. Stability can be evaluated by conducting repeated measurements under controlled conditions or by calculating the standard deviation or coefficient of variation of the results. This approach helps assess how consistent the sensor's performance is over time. The stability of our SPR sensor was assessed by analyzing the distribution of repeated measurements [ 61 ]. The results showed that 95% of the data points fell within two standard deviations of the mean, indicating stable performance over time. This stability is essential for ensuring accurate detection of small refractive index changes in long-term applications. Dynamic Range refers to the range of refractive indices over which an SPR sensor can effectively operate. It is determined by how large the change in refractive index (∆RI) or concentration (∆C) influences the signal output of the sensor. A wider dynamic range allows the sensor to detect a broader range of analyte concentrations, making it versatile for various applications ranging from environmental monitoring to biomedical diagnostics [ 62 , 63 ]. Generally, there is a trade-off between dynamic range and sensitivity; a higher dynamic range often results in lower sensitivity, and vice versa [ 62 , 64 , 65 ]. For example, a hybrid angular-interrogation SPR system achieved a dynamic range of ⁓ 1.33–1.40 RIU while maintaining high sensitivity [ 63 ]. Dynamic range can be tuned by modifying the sensor configuration, such as using high refractive index thin films or different fiber optic core materials in SPR fiber optic sensors [ 66 ]. Our sensor demonstrated a dynamic range of approximately 1.33–1.37 RIU, making it suitable for a wide array of applications, from simple aqueous buffers to complex biological samples. Signal-to-Noise Ratio (SNR) is a measure of the sensor's ability to distinguish signal from noise. It is defined as the ratio of the signal amplitude to the noise amplitude: SNR = \(\:\frac{\text{S}\text{i}\text{g}\text{n}\text{a}\text{l}\:\text{A}\text{m}\text{p}\text{l}\text{i}\text{t}\text{u}\text{d}\text{e}}{\text{N}\text{o}\text{i}\text{s}\text{e}\:\text{A}\text{m}\text{p}\text{l}\text{i}\text{t}\text{u}\text{d}\text{e}}\) (7) A higher SNR indicates more reliable detection capabilities, as it reflects the sensor's ability to accurately distinguish between signal and background noise [ 67 ]. SNR affects the sensor's sensitivity and resolution, as a higher SNR allows for the detection of smaller signals [ 68 ]. The SNR of our biosensor was calculated to be 20:1. It means the signal amplitude is 20 times greater than the noise amplitude, which is a favorable condition for robust signal detection capabilities and ensuring reliable measurements of small refractive index changes. This high SNR is crucial for enhancing the sensor's sensitivity and resolution, as demonstrated by recent studies that have shown significant improvements in SNR through advanced signal processing techniques [ 67 – 69 ]. 4. Conclusion This study introduces a novel dual-mode biosensor combining Surface Plasmon Resonance (SPR) and Goos-Hänchen (GH) shift sensing for rapid, label-free cancer cell detection. Significant SPR shifts were observed (2.2° for A549 and 1.6° for LS180), alongside GH shifts of 6.5 µm and 5.8 µm, respectively. With a limit of detection of 6.8 × 10⁻⁵ RIU and a dynamic range of 1.33–1.37 RIU, the biosensor is highly sensitive to small refractive index changes, making it suitable for diverse sample types. Compared to traditional SPR systems, this dual-mode approach enhances sensitivity and specificity, offering a promising, non-invasive alternative for real-time cancer diagnostics. Its high throughput and ability to detect subtle changes in refractive index position it as a powerful tool for early cancer detection and personalized medicine. Declarations Author Agreement Statement We the undersigned declare that this manuscript is original, has not been published before, and is not currently being considered for publication elsewhere. We confirm that the manuscript has been read and approved by all named authors and that there are no other persons who satisfied the criteria for authorship but are not listed. We further confirm that the order of authors listed in the manuscript has been approved by all of us. We understand that the corresponding author (Dr. Ebrahim Safari) and the first author (Majid Karimi) are the contacts for the Editorial process. They are responsible for communicating with the other authors about progress, submissions of revisions, and final approval of proofs. Signed by all authors as follows: 1. Majid Karimi, PhD student in Physics, Department of Optics and Laser Physics, Faculty of Physics, University of Tabriz, Tabriz, Iran. Email address: [email protected] 2*. Dr. Ebrahim Safari, Associate Professor of Physics, Department of Optics and Laser Physics, Faculty of Physics, University of Tabriz, Tabriz, Iran. Email address: [email protected] 3. Dr. Reza Safaralizadeh, Professor of Molecular Genetics, Department of Biology, Faculty of Natural Sciences, University of Tabriz, Tabriz, Iran. Email address: [email protected] . 4. Dr. Gholamreza Dehghan, Professor of Biochemistry, Department of Biology, Faculty of Natural Sciences, University of Tabriz, Tabriz, Iran. Email address: [email protected] . Author Contribution Statement Conception and design of the study: Ebrahim Safari, Majid Karimi, Reza Safaralizadeh and Gholamreza Dehghan. Acquisition of data: Majid Karimi , Ebrahim Safari. Analysis and interpretation of data: Majid Karimi, Ebrahim Safari, Reza Safaralizadeh and Gholamreza Dehghan. Drafting the manuscript: Majid Karimi. Signed by all authors as follows: 1. Majid Karimi, PhD student in Physics, Department of Optics and Laser Physics, Faculty of Physics, University of Tabriz, Tabriz, Iran. Email address: [email protected] 2*. Dr. Ebrahim Safari, Associate Professor of Physics, Department of Optics and Laser Physics, Faculty of Physics, University of Tabriz, Tabriz, Iran. Email address: [email protected] 3. Dr. Reza Safaralizadeh, Professor of Molecular Genetics, Department of Biology, Faculty of Natural Sciences, University of Tabriz, Tabriz, Iran. Email address: [email protected] . 4. Dr. Gholamreza Dehghan, Professor of Biochemistry, Department of Biology, Faculty of Natural Sciences, University of Tabriz, Tabriz, Iran. Email address: [email protected] . Declaration of Interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Signed by all authors as follows: 1. Majid Karimi, PhD student in Physics, Department of Optics and Laser Physics, Faculty of Physics, University of Tabriz, Tabriz, Iran. Email address: [email protected] . 2*. Dr. Ebrahim Safari, Associate Professor of Physics, Department of Optics and Laser Physics, Faculty of Physics, University of Tabriz, Tabriz, Iran. Email address: [email protected] . 3. Dr. Reza Safaralizadeh, Professor of Molecular Genetics, Department of Biology, Faculty of Natural Sciences, University of Tabriz, Tabriz, Iran. Email address: [email protected] . 4. Dr. Gholamreza Dehghan, Professor of Biochemistry, Department of Biology, Faculty of Natural Sciences, University of Tabriz, Tabriz, Iran. Email address: [email protected] . Funding Declaration No funding was received to assist with the preparation of this manuscript. The authors declare that they have no financial support or sponsorship to report. 1. Majid Karimi, PhD student in Physics, Department of Optics and Laser Physics, Faculty of Physics, University of Tabriz, Tabriz, Iran. Email address: [email protected] 2*. Dr. Ebrahim Safari, Associate Professor of Physics, Department of Optics and Laser Physics, Faculty of Physics, University of Tabriz, Tabriz, Iran. Email address: [email protected] 3. Dr. Reza Safaralizadeh, Professor of Molecular Genetics, Department of Biology, Faculty of Natural Sciences, University of Tabriz, Tabriz, Iran. Email address: [email protected] . 4. Dr. Gholamreza Dehghan, Professor of Biochemistry, Department of Biology, Faculty of Natural Sciences, University of Tabriz, Tabriz, Iran. Email address: [email protected] . 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Biomedical Opt Express 13(4):1784–1800 Meng Q-Q et al (2017) Figure of merit enhancement of a surface plasmon resonance sensor using a low-refractive-index porous silica film. Sensors 17(8):1846 Meng Q-Q et al (2017) Performance analysis of surface plasmon resonance sensor with high-order absentee layer. Chin Phys B 26(12):124213 Skoog DA, Holler FJ, Crouch S (2007) Principles of instrumental analysis. Thomson Brooks. Cole, Canada Kolomenskii AA, Gershon P, Schuessler H (1997) Sensitivity and detection limit of concentration and adsorption measurements by laser-induced surface-plasmon resonance. Appl Opt 36(25):6539–6547 Danlard I, Akowuah EK (2020) Assaying with PCF-based SPR refractive index biosensors: From recent configurations to outstanding detection limits. Opt Fiber Technol 54:102083 Homola J (2008) Surface plasmon resonance sensors for detection of chemical and biological species. Chem Rev 108(2):462–493 Guo B et al (2017) A simple surface plasmon resonance biosensor for detection of PML/RARα based on heterogeneous fusion gene-triggered nonlinear hybridization chain reaction. Sci Rep 7(1):14037 Sarcina L et al (2021) Surface plasmon resonance assay for label-free and selective detection of hiv-1 p24 protein. Biosensors 11(6):180 Thomsen V, Schatzlein D, Mercuro D (2003) Limits of detection in spectroscopy: a diagrammatic view of the concept of detection limits is presented that effectively shows its relationship to the reproducibility of measurements on the blank. Spectroscopy 18(12):112–115 Benaziez S, Dibi Z, Benaziez N (2018) Reflectivity optimization of the SPR graphene sensor. Nanopages 13(1):5–17 Motsa BB, Stahelin RV (2023) A beginner’s guide to surface plasmon resonance. Biochemist 45(1):18–22 Kim N-H et al (2019) Sensitivity and stability enhancement of surface plasmon resonance biosensors based on a large-area Ag/MoS2 substrate. Sensors 19(8):1894 Park J-H, Cho Y-W, Kim T-H (2022) Recent advances in surface plasmon resonance sensors for sensitive optical detection of pathogens. Biosensors 12(3):180 Wang G et al (2017) A sensitive and stable surface plasmon resonance sensor based on monolayer protected silver film. Sensors 17(12):2777 Li M-C et al (2021) A simple phase-sensitive surface plasmon resonance sensor based on simultaneous polarization measurement strategy. Sensors 21(22):7615 Prabowo BA, Purwidyantri A, Liu K-C (2018) Surface plasmon resonance optical sensor: A review on light source technology. Biosensors 8(3):80 Koresawa H et al (2023) Real-time hybrid angular-interrogation surface plasmon resonance sensor in the near-infrared region for wide dynamic range refractive index sensing. Sci Rep 13(1):15655 Deng S, Wang P, Yu X (2017) Phase-sensitive surface plasmon resonance sensors: Recent progress and future prospects. Sensors 17(12):2819 Ho H, Wu S Simulation of a novel high sensitivity and wide dynamic range phase-sensitive surface plasmon resonance sensor . in (2007) IEEE Conference on Electron Devices and Solid-State Circuits . 2007. IEEE Jorgenson RC, Yee SS (1994) Control of the dynamic range and sensitivity of a surface plasmon resonance based fiber optic sensor. Sens Actuators A: Phys 43(1–3):44–48 Aray A et al (2017) Gain-assisted surface plasmon resonance refractive index sensor. IEEE Sens J 17(14):4466–4471 Ma L et al (2021) Effect of spectral signal-to-noise ratio on resolution enhancement at surface plasmon resonance. Sensors 21(2):641 Abumazwed A et al (2016) Projection method for improving signal to noise ratio of localized surface plasmon resonance biosensors. Biomedical Opt Express 8(1):446–459 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 26 Jul, 2025 Read the published version in Plasmonics → Version 1 posted Editorial decision: Revision requested 16 Jun, 2025 Reviews received at journal 16 Jun, 2025 Reviews received at journal 09 Jun, 2025 Reviewers agreed at journal 07 Jun, 2025 Reviewers agreed at journal 06 Jun, 2025 Reviewers invited by journal 05 Jun, 2025 Editor assigned by journal 28 May, 2025 Submission checks completed at journal 28 May, 2025 First submitted to journal 24 May, 2025 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. 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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-6737507","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":467905851,"identity":"44590ce6-d4b6-4765-a95c-7c1368e0b223","order_by":0,"name":"Majid Karimi","email":"","orcid":"","institution":"University of Tabriz","correspondingAuthor":false,"prefix":"","firstName":"Majid","middleName":"","lastName":"Karimi","suffix":""},{"id":467905852,"identity":"72b8798d-99d0-43b8-96f0-e3574058e48b","order_by":1,"name":"Ebrahim Safari","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA70lEQVRIie3RsUoDMRzH8d+UW/5wa4qKr/AvB6fbvUqC4NRBcLBDKX8o3FjfxskhRTiXPoBT23Bwk0On0kGkOXESTOvmkO+QIeTDPyFAKvUPqyQsY4ByQAH8vW0i5OvMMpBB/VcCbnpySozMb91kdV6sZ107vptayV428M+/kwpUaNfcU9moq+GSlRW6ZZguNoVwtlUmEJQDYbKCUXiLi16s3btPQ0Wd7QLRVvL3YwSlXtSGWFE/ha3oo1OovF7MDelm9DAUNkWtO3YxUknWvrmdqfLZ65OXj+nFY37j/T5CcLn5sdH/TgykUqlU6oQOlCpKhuSceakAAAAASUVORK5CYII=","orcid":"","institution":"University of Tabriz","correspondingAuthor":true,"prefix":"","firstName":"Ebrahim","middleName":"","lastName":"Safari","suffix":""},{"id":467905853,"identity":"04ac230e-423b-4842-b37a-c2ba6e93067f","order_by":2,"name":"Reza Safaralizadeh","email":"","orcid":"","institution":"University of Tabriz","correspondingAuthor":false,"prefix":"","firstName":"Reza","middleName":"","lastName":"Safaralizadeh","suffix":""},{"id":467905854,"identity":"259687b3-9caa-4aae-bc0e-4c037648ca25","order_by":3,"name":"Gholamreza Dehghan","email":"","orcid":"","institution":"University of Tabriz","correspondingAuthor":false,"prefix":"","firstName":"Gholamreza","middleName":"","lastName":"Dehghan","suffix":""}],"badges":[],"createdAt":"2025-05-24 07:38:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6737507/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6737507/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11468-025-03202-1","type":"published","date":"2025-07-26T15:57:22+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":84187818,"identity":"aabacd5d-2085-4bc8-aef9-570c2ffd8738","added_by":"auto","created_at":"2025-06-09 06:00:58","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":493395,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Electron beam evaporation system used for gold deposition on glass substrates, (b) The optical microscope employed for visualizing the morphology and distribution of cancer cells on the sensor surface.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6737507/v1/23582e9e3a7e6cd32db75fc7.png"},{"id":84187822,"identity":"48ba0f0b-a2e9-4fda-bc44-f65398a46058","added_by":"auto","created_at":"2025-06-09 06:00:58","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":459539,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Bare gold-coated samples fresh from the evaporation system, showing their general appearance, (b) Optical microscopy image of A549 cancer cells adhered to the gold-coated substrate, and (c) Optical microscopy image of LS180 cancer cells adhered to the gold-coated substrate.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6737507/v1/ca83cb050965c4214e90e16b.png"},{"id":84187821,"identity":"fd68ed53-4f42-4a54-92e2-9b1e0654f988","added_by":"auto","created_at":"2025-06-09 06:00:58","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":257937,"visible":true,"origin":"","legend":"\u003cp\u003eAFM Topographic Images of (a) Bare Gold-Coated Sample, (b) Gold-Coated substrate + A549 Cancer Cells, and (c) Gold-Coated substrate + LS180 Cancer Cells at 8.7 μm × 8.7 μm Scale.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6737507/v1/e6a08e2e15b6159e1bee84fc.png"},{"id":84188504,"identity":"fc02a5fc-5873-4266-9510-8b5403750b7f","added_by":"auto","created_at":"2025-06-09 06:08:59","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":399854,"visible":true,"origin":"","legend":"\u003cp\u003eThe experimental setup for Surface Plasmon Resonance and Goos-Hänchen shift measurements.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6737507/v1/2ceb9aecff7d8adf5a18b17c.png"},{"id":84187816,"identity":"6b5952af-8ffc-406c-884e-d7acd061a8f0","added_by":"auto","created_at":"2025-06-09 06:00:57","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":92245,"visible":true,"origin":"","legend":"\u003cp\u003eThe schematic of setup for Surface Plasmon Resonance and Goos-Hänchen shift measurements.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6737507/v1/42b66399ec351f1dbfb85de4.png"},{"id":84187833,"identity":"1530d717-12d5-448c-a6fb-847c0d37125a","added_by":"auto","created_at":"2025-06-09 06:00:59","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":50286,"visible":true,"origin":"","legend":"\u003cp\u003eThe obtained Surface Plasmon Resonance reflectivity curves for all samples.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6737507/v1/5d4b4d7e7f3057591d2dc0d7.png"},{"id":87756795,"identity":"4170f597-e45b-4cb6-b685-a38e45e46a14","added_by":"auto","created_at":"2025-07-28 16:09:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2850653,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6737507/v1/ec860c22-7811-4a80-8b82-c36a7e0a19ba.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development of an Optical Biosensor Based on the Goos-Hänchen Shift and Surface Plasmon Resonance for Rapid Detection of Cancer Cells","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eCancer is a leading cause of morbidity and mortality globally, with lung and colorectal cancers being among the most prevalent and deadly forms. According to the World Health Organization, lung cancer accounts for approximately 12.4% of all cancer diagnoses and 18.7% of cancer-related deaths, while colorectal cancer contributes to 9.6% of new cancer cases and 9.3% of cancer deaths worldwide [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. These statistics underscore the urgent need for advanced diagnostic technologies capable of enabling rapid, accurate, and non-invasive detection of cancer cells. Early diagnosis significantly improves patient outcomes by facilitating timely therapeutic interventions, ultimately increasing survival rates and enhancing quality of life [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTraditional diagnostic techniques for cancer detection, including tissue biopsy, histopathological analysis, and molecular imaging, are often invasive, time-consuming, and expensive [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Furthermore, these methods often lack the sensitivity and specificity needed for early-stage diagnosis, leading to false positives/negatives and delays in treatment [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. This highlights the growing demand for innovative, highly sensitive, and rapid diagnostic tools capable of detecting cancer cells in a label-free manner, preserving the natural state of biological samples [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOptical biosensors have gained significant attention as powerful diagnostic tools due to their high sensitivity, real-time monitoring, non-invasive operation and label-free detection capabilities. These sensors detect minute changes in optical properties, such as refractive index variations induced by biomolecule binding [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Among various optical sensing techniques, Surface Plasmon Resonance (SPR) has emerged as a widely adopted approach for detecting biomolecular interactions with high sensitivity and specificity. SPR utilizes the excitation of surface plasmons collective oscillations of free electrons at the metal-dielectric interface under total internal reflection (TIR) to generate an evanescent wave that penetrates a few hundred nanometers into the adjacent dielectric medium, making it highly sensitive to changes near the sensor surface [\u003cspan additionalcitationids=\"CR11 CR12\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Other optical techniques, such as optical coherence tomography (OCT), surface-enhanced raman spectroscopy (SERS), and reflectometric interference spectroscopy, also offer promising solutions for early cancer detection [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. These advancements position optical biosensors as robust platforms for early disease diagnostics, offering advantages like reusability and ultrafast sensing capabilities [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSPR biosensors direct polarized light from a laser source towards a high-refractive-index prism coated with noble metal, typically gold, known for its excellent plasmonic properties and chemical stability. When the incident light satisfies the resonance condition, energy is transferred to the surface plasmons, resulting in a dip in the reflected light intensity at a specific angle, known as the SPR angle. A variation in the local refractive index such as the binding of cancer cells to the gold surface shifts the SPR angle, providing a sensitive and label-free means of detection. SPR has been extensively utilized for detecting a wide range of biological targets, including proteins, nucleic acids, and cancer biomarkers [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. However, traditional SPR techniques are limited by their reliance on intensity or angular shifts alone, which may be influenced by environmental noise and systematic errors.\u003c/p\u003e \u003cp\u003eThis study presents a novel powerful optical biosensor combining GH Shift and SPR for accurate, rapid, sensitive, and label-free cancer cell detection. The GH shift refers to the lateral displacement of the reflected light beam at the interface between two media under TIR conditions [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. It is extremely sensitive to changes in the refractive index at the sensor surface, making it ideal for detecting subtle changes induced by cancer cell binding or other biomolecules [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. By combining the refractive index changes detected by SPR with the lateral displacement measured by the GH shift, the biosensor enhances detection sensitivity, providing additional information to improve overall accuracy [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. By effectively distinguishing between lung and colon cancer cells, this system demonstrates significant potential for clinical cancer diagnostics and personalized medicine, paving the way for advancements in biomedical optics and cancer detection technologies.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Materials\u003c/h2\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003e2.1.1. Optical Components\u003c/h2\u003e \u003cp\u003eA red diode laser (wavelength\u0026thinsp;\u0026asymp;\u0026thinsp;633 nm, power\u0026thinsp;\u0026asymp;\u0026thinsp;50 mW) was used as the coherent light source, providing a stable and monochromatic beam essential for precise SPR and GH shift measurements. A non-polarizing beam splitter (50:50 ratio) divided the incident laser beam into two identical optical paths, enabling simultaneous measurement on both control and test targets for differential analysis. A linear polarizer, placed after the laser, transmitted p-polarized light, optimizing SPR excitation by eliminating s-polarized components. A high-refractive-index BK7 glass prism (refractive index\u0026thinsp;=\u0026thinsp;1.515 at λ\u0026thinsp;=\u0026thinsp;633 nm) was used to achieve total internal reflection (TIR), generating the evanescent wave necessary for SPR excitation. The prism was mounted on a precision rotational stage with a resolution of \u0026asymp;\u0026thinsp;0.016\u0026deg;, allowing accurate angular adjustments. A high-sensitivity quadrant detector (QD) was used to measure lateral beam shifts with micrometer precision, essential for quantifying GH shift variations. The QD was interfaced with a data acquisition system for real-time voltage readouts, recording four distinct voltage outputs corresponding to the four quadrants of the detector, enabling the calculation of lateral shifts.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.1.1 Substrate Preparation\u003c/h2\u003e \u003cp\u003eHigh-purity 24-karat gold (99.99% purity) was deposited onto glass slides (thickness\u0026thinsp;=\u0026thinsp;1 mm) using electron beam evaporation (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea) under high vacuum conditions (10⁻⁶ Torr) to achieve a uniform nanoscale coating (\u0026asymp;\u0026thinsp;50 nm). Using high-purity gold at the nanoscale ensures optimal SPR excitation due to the metal's superior plasmonic properties, chemical inertness, and biocompatibility. Gold's high electron density and ability to support surface plasmon polaritons make it an ideal choice for enhancing sensitivity in optical biosensing applications [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The thickness was monitored using a quartz crystal microbalance to ensure consistency across all samples. After deposition, the gold-coated substrates were annealed in a furnace under an argon atmosphere to improve the gold film's quality and uniformity. Annealing was performed at 375\u0026deg;C for 4 hours, with continuous argon flow at 100 mL/min. The annealing process optimized the gold layer's stability, crystallinity, and surface morphology, ensuring it adhered well to the glass and prevented detachment during subsequent cell culturing. The morphology and thickness of the gold layer were also characterized using optical microscopy imaging (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb) and Atomic force microscopy (AFM).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.1.3. Cancer Cell Lines and Culture Conditions\u003c/h2\u003e \u003cp\u003eHuman lung adenocarcinoma (A549) [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] and human colon adenocarcinoma (LS180) [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] cell lines were selected for their clinical relevance in cancer diagnostics and their distinct biological characteristics. These cells were cultured on the gold-coated substrates to induce localized refractive index changes upon interaction with the evanescent wave generated under TIR conditions [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Cells were cultured in Dulbecco\u0026rsquo;s Modified Eagle Medium (DMEM) supplemented with 10% fetal bovine serum (FBS), 1% penicillin-streptomycin, and 1% L-glutamine, all sourced from Sigma-Aldrich. The cells were maintained at 37\u0026deg;C in a humidified atmosphere with 5% CO₂. Gold-coated substrates were sterilized with 70% ethanol, followed by rinsing with phosphate-buffered saline (PBS, pH 7.4). Cells were seeded at a density of 5\u0026times;10⁵ cells/cm\u0026sup2; and allowed to adhere for 24 hours. For control measurements, substrates without cancer cells were prepared under identical conditions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.1.4. Reagents and Chemicals\u003c/h2\u003e \u003cp\u003e \u003cstrong\u003ePhosphate-buffered saline (PBS)\u003c/strong\u003e \u003cp\u003eUsed for washing and maintaining isotonic conditions during cell seeding and measurement procedures.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEthanol (70%)\u003c/strong\u003e \u003cp\u003eUtilized for substrate sterilization to prevent contamination.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eThe reagents and chemicals were purchased from Merck or Sigma-Aldrich, ensuring high purity and consistency.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Experimental Setup and Procedure\u003c/h2\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1 Optical Configuration\u003c/h2\u003e \u003cp\u003eThe red diode laser beam was directed toward a beam splitter, producing two identical beams. One beam was guided to the control target (bare gold-coated substrate), while the other was directed to the test target (gold-coated substrate with cancer cells). After the beam splitter, each beam passed through a polarizer to ensure p-polarization, which is essential for SPR excitation. The beams then passed through a high-refractive-index prism, rotated by a precision rotational stage. The angle of incidence (θ) was varied between 30\u0026deg; and 80\u0026deg; in 0.016\u0026deg; increments. Reflected beams from the gold interface were detected by quadrant detectors positioned at fixed distances from the prism. Voltage outputs from the four quadrants were recorded at each incident angle using a digital voltmeter (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA key feature of this setup is the use of a beam splitter to generate two identical optical paths: one directed at a control target with a bare gold-coated substrate and the other at a test target with cancer cells cultured on the gold surface. This differential configuration helps minimize environmental noise and systematic errors by providing a real-time reference, enabling precise detection of GH shift variations caused by the presence of cancer cells.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2. SPR Measurement\u003c/h2\u003e \u003cp\u003eSPR was excited when p-polarized light underwent total internal reflection (TIR) at the gold-dielectric interface, generating an evanescent wave sensitive to refractive index changes. By rotating the prism and recording the voltage outputs at each incident angle, SPR curves were obtained for both control and test targets. The reflected intensity was measured as a function of the incident angle, yielding the SPR curves. SPR dip positions were recorded for both control and test targets, allowing the detection of cancer cells based on local refractive index changes. The shift in SPR angle (Δθₛₚ\u003csub\u003er\u003c/sub\u003e) between control and test samples was calculated to quantify the cancer cell-induced refractive index changes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e2.2.3 GH Shift Measurement\u003c/h2\u003e \u003cp\u003eGH shift measurements provided complementary information by detecting lateral displacements of the reflected beam due to phase changes during TIR. This dual-mode sensing approach enhances the system's sensitivity and specificity and allows for precise differentiation between different cancer cells, facilitating rapid detection without the need for complex labeling procedures. Lateral shifts were quantified by analyzing the differential voltage outputs from the quadrant detectors, using the following equation [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]:\u003c/p\u003e \u003cp\u003eΔx\u0026thinsp;=\u0026thinsp;k \u0026times; (V\u003csub\u003eQ1\u003c/sub\u003e​+V\u003csub\u003eQ4\u003c/sub\u003e​\u0026minus;V\u003csub\u003eQ2\u003c/sub\u003e​\u0026minus;V\u003csub\u003eQ3\u003c/sub\u003e​) (1)\u003c/p\u003e \u003cp\u003eWhere V\u003csub\u003eQ1\u003c/sub\u003e​ to V\u003csub\u003eQ4\u003c/sub\u003e ​ are the voltages from the four quadrants, and k is a calibration constant determined experimentally. By comparing GH shifts between control and test targets, cancer cell-induced refractive index changes were isolated, enhancing sensitivity and reducing environmental noise.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e2.2.4 Validation and Reproducibility\u003c/h2\u003e \u003cp\u003eExperiments were repeated five times under identical conditions to ensure consistency and reproducibility of the results.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e2.2.5 Data Analysis and Statistical Methods\u003c/h2\u003e \u003cp\u003eSPR curves were analyzed using Lorentzian fitting [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], while GH shift data were processed using Gaussian fitting [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Data were analyzed using one-way ANOVA, followed by Tukey\u0026rsquo;s post-hoc test for multiple comparisons. A p-value of \u0026lt;\u0026thinsp;0.05 was considered statistically significant [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Key performance metrics for the biosensor, including sensitivity, full width at half minimum (FWHM), figure of merit (FOM), dynamic range, Limit of Detection (LOD), and signal-to-noise ratio (SNR), were evaluated. These parameters are critical for assessing the sensor's ability to detect small changes in refractive index, distinguish between different cell types, and operate effectively across a wide range of analyte concentrations [\u003cspan additionalcitationids=\"CR37 CR38\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3. Results and Discussion","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Cell Adhesion and Morphology\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea shows the bare gold-coated samples fresh from the evaporation system, showing their general appearance. To confirm the successful attachment and uniform distribution of cancer cells on the gold-coated glass substrates, we first performed optical microscopy imaging. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec, demonstrate that both A549 (lung cancer) and LS180 (colon cancer) cells exhibited good adhesion and homogeneous distribution across the surface. The cell morphology appeared intact, indicating that the gold surface supported cellular attachment without inducing significant cytotoxicity. The formation of dense cellular layers suggests substantial alteration of the refractive index at the interface, which is critical for both SPR sensing and GH shift measurements.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.2. AFM Imaging and Surface Morphology\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents AFM topographic images (8.7 \u0026micro;m \u0026times; 8.7 \u0026micro;m scale), illustrating the surface morphology of the samples. The images provide key qualitative insights into the sensor surface before and after cell adhesion. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea depicts the bare gold-coated sample, revealing a typical granular topography associated with evaporated gold films. The height range of the surface varied from \u0026minus;\u0026thinsp;4.41 nm to 4.86 nm, indicating that the gold film had a relatively smooth surface with low roughness, which is essential for cellular adhesion studies and minimizing interference in biosensing applications. Upon A549 cell attachment (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb), the AFM analysis revealed a height range from \u0026minus;\u0026thinsp;56.9 nm to 61.8 nm, with a polynormalfit of 119 nm. This significant height variation suggests that A549 cells are more spread out on the surface due to strong cell-substrate adhesion. This morphology likely increases the interaction area with the evanescent wave in both SPR and GH shift measurements, thereby enhancing the sensor\u0026rsquo;s sensitivity.\u003c/p\u003e \u003cp\u003eFor the LS180 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec), the AFM analysis showed a height variation of -11.5 nm to 8.44 nm, with a polynormalfit of 20 nm. These cells exhibited a more compact morphology on the gold surface compared to A549 cells, leading to a smaller contact area. This compact morphology likely results in reduced shifts in both SPR and GH measurements compared to A549 cells.\u003c/p\u003e \u003cp\u003eThe AFM results underscore the significant differences in cell morphology between A549 and LS180 cells, which are likely to affect their interactions with the optical biosensor. The larger, spread-out morphology of A549 cells suggests that they interact more extensively with the sensor surface, resulting in larger optical shifts, while the compact morphology of LS180 cells likely leads to reduced interactions, contributing to smaller shifts. This variation in cell morphology is an important factor in understanding the sensitivity and performance of optical biosensors for cancer cell detection, as cell shape and surface interactions directly influence the sensor's optical response.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.3 SPR Measurements\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e illustrates the experimental setup used for SPR detection, including a red diode laser, polarizer, beam splitter, high-refractive-index prism, quadrant detector and voltmeter. A schematic of this setup is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. The obtained SPR reflectivity curves for the control (bare gold-coated substrate) and test samples (A549 and LS180 cancer cells) are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. The bare gold-coated substrate exhibited an SPR dip at approximately 45.2\u0026deg;, indicating resonance at the gold-dielectric interface. Upon culturing A549 cells, a significant red shift of 2.2\u0026deg; was recorded, moving the resonance angle to about 47.4\u0026deg;. For LS180 cells, a red shift of 1.6\u0026deg; was observed, with the new resonance angle at approximately 46.8\u0026deg;. These shifts demonstrate that cancer cell adhesion altered the local refractive index, which modified the plasmonic resonance conditions at the metal-dielectric interface. The larger shift for A549 cells suggests a higher refractive index contribution, likely due to differences in cell composition, adhesion properties, and intracellular density. SPR measurements were repeated five times under identical conditions, yielding mean resonance shifts of 2.2\u0026deg; \u0026plusmn; 0.03\u0026deg; for A549 cells and 1.6\u0026deg; \u0026plusmn; 0.03\u0026deg; for LS180 cells. The low standard deviations indicate minimal variability, demonstrating the high precision of the system. SPR experiments with non-cancerous epithelial cells (negative control) showed negligible resonance shifts, confirming that the observed shifts were due to cancer-specific interactions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.4 GH Shift Measurements\u003c/h2\u003e \u003cp\u003eThe GH shift, which quantifies the lateral displacement of the reflected beam, complements SPR data by detecting phase changes during TIR. For the bare gold-coated control sample, the GH shift was negligible (\u0026lt;\u0026thinsp;1 \u0026micro;m), as expected in the absence of refractive index changes. However, in the presence of cancer cells, a significant increase in lateral displacement was recorded. The GH shifts for A549 and LS180 cells were 6.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1 \u0026micro;m and 5.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1 \u0026micro;m, respectively. These shifts correlate well with the SPR dip shifts, demonstrating that GH shift measurements provide an additional layer of sensitivity to subtle changes in refractive index, complementing the SPR response.\u003c/p\u003e \u003cp\u003eThe correlation between SPR and GH shifts is summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Our biosensor successfully detected 500,000 cells/cm\u0026sup2;, aligning with or surpassing previous SPR-based cancer detection methods.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of SPR and GH Shift Biosensor Results for Cancer Cell Detection.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eValue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSPR Resonance Angle Shift for A549\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.2\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eShift from bare substrate resonance angle.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSPR Resonance Angle Shift for LS180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.6\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eShift from bare substrate resonance angle.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGH Shift for A549 Cancer Cells\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.5 \u0026micro;m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLateral displacement of the reflected beam for A549.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGH Shift for LS180 Cancer Cells\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.8 \u0026micro;m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLateral displacement of the reflected beam for LS180.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDetection Limit (Cell Density)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e500,000 cells/cm\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMinimum cell density detectable by the sensor.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSensitivity - A549 Cancer Cells\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e220\u0026deg;/RIU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCalculated sensitivity for A549 detection.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSensitivity - LS180 Cancer Cells\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e160\u0026deg;/RIU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCalculated sensitivity for LS180 detection.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFWHM \u0026ndash; Both A549 and LS180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.5\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFull Width at Half Minimum of SPR curve for both cancer cell types.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFigure of Merit (FOM) - LS180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e106.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA comprehensive measure of sensor performance for LS180 cells.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFigure of Merit (FOM) - A549\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e146.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA comprehensive measure of sensor performance for A549 cells.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLimit of Detection (LOD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.8 \u0026times; 10⁻⁵ RIU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMinimum detectable refractive index change.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMinimum Reflectivity for all samples\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR\u003csub\u003emin\u003c/sub\u003e value observed across all conditions\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e95%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage of data points within two standard deviations of the mean\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDynamic Range\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.33\u0026ndash;1.37 RIU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRange of refractive indices detectable by the sensor.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSignal-to-Noise Ratio (SNR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20:1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe ratio of signal amplitude to noise amplitude, indicating signal quality.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Assessment of Key Analytical Parameters\u003c/h2\u003e \u003cp\u003eThe sensitivity (S) of our biosensor, defined as the ratio of the shift in the SPR angle to the change in refractive index (S\u0026thinsp;=\u0026thinsp;Δθ\u003csub\u003eSPR\u003c/sub\u003e/Δn (2)) [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], was calculated to be 160\u0026deg;/RIU for LS180 and 220\u0026deg;/RIU for A549. In this work, Δn was assumed to be 0.01 RIU, based on typical values reported for biomolecular interactions in similar SPR experiments [\u003cspan additionalcitationids=\"CR43\" citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. These values indicate that the biosensor is capable of detecting small refractive index changes caused by the presence of cancer cells, as summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eThe full width at half minimum (FWHM) assesses the resolution of the SPR sensor, where a smaller FWHM indicates better resolution [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. We calculated the FWHM from the SPR curves and found it to be approximately 1.5\u0026deg; for both LS180 and A549 cells.\u003c/p\u003e \u003cp\u003eThe figure of merit (FOM) is a comprehensive measure of a sensor's performance, calculated as the ratio of sensitivity to the FWHM of the SPR reflectance dip: (FOM\u0026thinsp;=\u0026thinsp;S/FWHM (3)). FOM combines sensitivity and resolution, providing a holistic assessment of sensor performance. A higher FOM indicates better performance, reflecting both high sensitivity and a narrow resonance width [\u003cspan additionalcitationids=\"CR47\" citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. For our biosensor, the FOM was calculated to be around 106.7 for LS180 cells and 146.7 for A549 cells. These values are also reported in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eThe Limit of Detection (LOD) in sensors refers to the smallest change in refractive index that can be reliably detected. It is a critical parameter for evaluating SPR sensor performance, indicating the effectiveness in detecting small refractive index changes [\u003cspan additionalcitationids=\"CR50 CR51\" citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. The LOD is typically determined by the background noise level and is defined as three times the standard deviation (3σ) of the blank noise, divided by the sensitivity of the system: (LOD\u0026thinsp;=\u0026thinsp;3σ/S (4)) [\u003cspan additionalcitationids=\"CR54\" citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. The calculated LOD for our biosensor was approximately 6.8 \u0026times; 10⁻⁴ RIU, demonstrating the sensor\u0026rsquo;s ability to detect minimal refractive index changes Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eMinimum Reflectivity (R\u003csub\u003emin\u003c/sub\u003e) is a crucial parameter in surface plasmon resonance measurements, representing the lowest reflectivity value observed at the resonance angle [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. This minimum occurs when the conditions for surface plasmon excitation are met, leading to a significant drop in the reflected light intensity. The position of this minimum reflectivity is sensitive to changes in the refractive index of the medium adjacent to the metal film, making it an essential indicator for detecting molecular interactions. The shift in the angle corresponding to minimum reflectivity (R\u003csub\u003emin\u003c/sub\u003e) can be used to quantify changes in the local refractive index caused by analyte binding [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. This allows for sensitive detection of biomolecular interactions. A sharper minimum reflectivity indicates better sensor performance, as it enhances sensitivity and resolution. The steepness of the reflectivity curve around R\u003csub\u003emin\u003c/sub\u003e is inversely related to the FWHM, which further influences detection accuracy. Achieving a low R\u003csub\u003emin\u003c/sub\u003e is critical for optimizing SPR sensor designs. Factors such as metal film thickness and material choice significantly impact R\u003csub\u003emin\u003c/sub\u003e, with optimal configurations resulting in sharper and deeper minima. The resonance angle (θ\u003csub\u003eres\u003c/sub\u003e) corresponding to minimum reflectivity (R\u003csub\u003emin\u003c/sub\u003e) can be determined from the reflectivity vs. angle of incidence curve (R(θ)). The relationship can be expressed as:\u003c/p\u003e \u003cp\u003eR(θ)=∣r\u003csub\u003ep\u003c/sub\u003e∣\u003csup\u003e2\u003c/sup\u003e (5)\u003c/p\u003e \u003cp\u003ewhere r\u003csub\u003ep\u003c/sub\u003e is the reflection coefficient for p-polarized light and is given by:\u003c/p\u003e \u003cp\u003er\u003csub\u003ep\u003c/sub\u003e= \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{{\\epsilon\\:}2\\text{k}1-{\\epsilon\\:}1\\text{k}2}{{\\epsilon\\:}2\\text{k}1+{\\epsilon\\:}1\\text{k}2}\\)\u003c/span\u003e\u003c/span\u003e (6)\u003c/p\u003e \u003cp\u003eWhere ε\u003csub\u003e1\u003c/sub\u003e and ε\u003csub\u003e2\u003c/sub\u003e are the dielectric constants of the two media and k\u003csub\u003e1\u003c/sub\u003e and k\u003csub\u003e2\u003c/sub\u003e are the z-components of the wave vectors in each medium.\u003c/p\u003e \u003cp\u003eIn our experiments, the R\u003csub\u003emin\u003c/sub\u003e values remained consistent across all conditions, with a value of approximately 0.1 (10%) for the bare gold-coated substrate, as well as for both A549 lung and LS180 colon cancer cells. The sharpness of the dips in the reflectivity curves further confirms the high resolution of the SPR sensor, which is crucial for detecting subtle refractive index changes induced by analyte binding.\u003c/p\u003e \u003cp\u003eStability refers to the ability of an SPR sensor to maintain consistent performance over time and under varying environmental conditions. It is crucial for ensuring reliable and accurate measurements, especially in long-term applications. Stability ensures that the sensor provides consistent results over extended periods, which is vital for monitoring biomolecular interactions or detecting analytes in real-time [\u003cspan additionalcitationids=\"CR58\" citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. A stable sensor can withstand changes in temperature, humidity, and other environmental factors without compromising its performance. Using protection layers like self-assembled monolayers (SAMs) can enhance stability by preventing metal oxidation and degradation [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. Choosing materials with inherent stability, such as gold, is common due to its resistance to oxidation compared to silver [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. Stability can be evaluated by conducting repeated measurements under controlled conditions or by calculating the standard deviation or coefficient of variation of the results. This approach helps assess how consistent the sensor's performance is over time. The stability of our SPR sensor was assessed by analyzing the distribution of repeated measurements [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. The results showed that 95% of the data points fell within two standard deviations of the mean, indicating stable performance over time. This stability is essential for ensuring accurate detection of small refractive index changes in long-term applications.\u003c/p\u003e \u003cp\u003eDynamic Range refers to the range of refractive indices over which an SPR sensor can effectively operate. It is determined by how large the change in refractive index (∆RI) or concentration (∆C) influences the signal output of the sensor. A wider dynamic range allows the sensor to detect a broader range of analyte concentrations, making it versatile for various applications ranging from environmental monitoring to biomedical diagnostics [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. Generally, there is a trade-off between dynamic range and sensitivity; a higher dynamic range often results in lower sensitivity, and vice versa [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. For example, a hybrid angular-interrogation SPR system achieved a dynamic range of ⁓ 1.33\u0026ndash;1.40 RIU while maintaining high sensitivity [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. Dynamic range can be tuned by modifying the sensor configuration, such as using high refractive index thin films or different fiber optic core materials in SPR fiber optic sensors [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. Our sensor demonstrated a dynamic range of approximately 1.33\u0026ndash;1.37 RIU, making it suitable for a wide array of applications, from simple aqueous buffers to complex biological samples.\u003c/p\u003e \u003cp\u003eSignal-to-Noise Ratio (SNR) is a measure of the sensor's ability to distinguish signal from noise. It is defined as the ratio of the signal amplitude to the noise amplitude: SNR = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{\\text{S}\\text{i}\\text{g}\\text{n}\\text{a}\\text{l}\\:\\text{A}\\text{m}\\text{p}\\text{l}\\text{i}\\text{t}\\text{u}\\text{d}\\text{e}}{\\text{N}\\text{o}\\text{i}\\text{s}\\text{e}\\:\\text{A}\\text{m}\\text{p}\\text{l}\\text{i}\\text{t}\\text{u}\\text{d}\\text{e}}\\)\u003c/span\u003e\u003c/span\u003e (7)\u003c/p\u003e \u003cp\u003eA higher SNR indicates more reliable detection capabilities, as it reflects the sensor's ability to accurately distinguish between signal and background noise [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. SNR affects the sensor's sensitivity and resolution, as a higher SNR allows for the detection of smaller signals [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. The SNR of our biosensor was calculated to be 20:1. It means the signal amplitude is 20 times greater than the noise amplitude, which is a favorable condition for robust signal detection capabilities and ensuring reliable measurements of small refractive index changes. This high SNR is crucial for enhancing the sensor's sensitivity and resolution, as demonstrated by recent studies that have shown significant improvements in SNR through advanced signal processing techniques [\u003cspan additionalcitationids=\"CR68\" citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eThis study introduces a novel dual-mode biosensor combining Surface Plasmon Resonance (SPR) and Goos-H\u0026auml;nchen (GH) shift sensing for rapid, label-free cancer cell detection. Significant SPR shifts were observed (2.2\u0026deg; for A549 and 1.6\u0026deg; for LS180), alongside GH shifts of 6.5 \u0026micro;m and 5.8 \u0026micro;m, respectively. With a limit of detection of 6.8 \u0026times; 10⁻⁵ RIU and a dynamic range of 1.33\u0026ndash;1.37 RIU, the biosensor is highly sensitive to small refractive index changes, making it suitable for diverse sample types. Compared to traditional SPR systems, this dual-mode approach enhances sensitivity and specificity, offering a promising, non-invasive alternative for real-time cancer diagnostics. Its high throughput and ability to detect subtle changes in refractive index position it as a powerful tool for early cancer detection and personalized medicine.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Agreement Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe the undersigned declare that this manuscript is original, has not been published before, and is not currently being considered for publication elsewhere.\u003c/p\u003e\n\u003cp\u003eWe confirm that the manuscript has been read and approved by all named authors and that there are no other persons who satisfied the criteria for authorship but are not listed. We further confirm that the order of authors listed in the manuscript has been approved by all of us.\u003c/p\u003e\n\u003cp\u003eWe understand that the corresponding author (Dr. Ebrahim Safari) and the first author (Majid Karimi) are the contacts for the Editorial process.\u003c/p\u003e\n\u003cp\u003eThey are responsible for communicating with the other authors about progress, submissions of revisions, and final approval of proofs.\u003c/p\u003e\n\u003cp\u003eSigned by all authors as follows:\u003c/p\u003e\n\u003cp\u003e1. Majid Karimi, PhD student in Physics, Department of Optics and Laser Physics, Faculty of Physics, University of Tabriz, Tabriz, Iran. Email address: [email protected]\u003c/p\u003e\n\u003cp\u003e2*. Dr. Ebrahim Safari, Associate Professor of Physics, Department of Optics and Laser Physics, Faculty of Physics, University of Tabriz, Tabriz, Iran. Email address: [email protected]\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e3. Dr. Reza Safaralizadeh, Professor of Molecular Genetics, Department of Biology, Faculty of Natural Sciences, University of Tabriz, Tabriz, Iran. Email address: [email protected].\u003c/p\u003e\n\u003cp\u003e4. Dr. Gholamreza Dehghan, Professor of Biochemistry, Department of Biology, Faculty of Natural Sciences, University of Tabriz, Tabriz, Iran. Email address: [email protected].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contribution Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConception and design of the study:\u003c/strong\u003e Ebrahim Safari, Majid Karimi, Reza Safaralizadeh and Gholamreza Dehghan.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcquisition of data:\u003c/strong\u003e Majid Karimi , Ebrahim Safari.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnalysis and interpretation of data:\u003c/strong\u003e Majid Karimi, Ebrahim Safari, Reza Safaralizadeh and Gholamreza Dehghan.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDrafting the manuscript:\u003c/strong\u003e Majid Karimi.\u003c/p\u003e\n\u003cp\u003eSigned by all authors as follows:\u003c/p\u003e\n\u003cp\u003e1. Majid Karimi, PhD student in Physics, Department of Optics and Laser Physics, Faculty of Physics, University of Tabriz, Tabriz, Iran. Email address: [email protected]\u003c/p\u003e\n\u003cp\u003e2*. Dr. Ebrahim Safari, Associate Professor of Physics, Department of Optics and Laser Physics, Faculty of Physics, University of Tabriz, Tabriz, Iran. Email address: [email protected]\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e3. Dr. Reza Safaralizadeh, Professor of Molecular Genetics, Department of Biology, Faculty of Natural Sciences, University of Tabriz, Tabriz, Iran. Email address: [email protected].\u003c/p\u003e\n\u003cp\u003e4. Dr. Gholamreza Dehghan, Professor of Biochemistry, Department of Biology, Faculty of Natural Sciences, University of Tabriz, Tabriz, Iran. Email address: [email protected] .\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003eSigned by all authors as follows:\u003c/p\u003e\n\u003cp\u003e1. Majid Karimi, PhD student in Physics, Department of Optics and Laser Physics, Faculty of Physics, University of Tabriz, Tabriz, Iran. Email address:\u0026nbsp;[email protected].\u003c/p\u003e\n\u003cp\u003e2*. Dr. Ebrahim Safari, Associate Professor of Physics, Department of Optics and Laser Physics, Faculty of Physics, University of Tabriz, Tabriz, Iran. Email address:\u0026nbsp;[email protected].\u003c/p\u003e\n\u003cp\u003e3. Dr. Reza Safaralizadeh, Professor of Molecular Genetics, Department of Biology, Faculty of Natural Sciences, University of Tabriz, Tabriz, Iran. Email address:\u0026nbsp;[email protected].\u003c/p\u003e\n\u003cp\u003e4. Dr. Gholamreza Dehghan, Professor of Biochemistry, Department of Biology, Faculty of Natural Sciences, University of Tabriz, Tabriz, Iran. Email address:\u0026nbsp;[email protected].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding was received to assist with the preparation of this manuscript. The authors declare that they have no financial support or sponsorship to report.\u003c/p\u003e\n\u003cp\u003e1. Majid Karimi, PhD student in Physics, Department of Optics and Laser Physics, Faculty of Physics, University of Tabriz, Tabriz, Iran. Email address: [email protected]\u003c/p\u003e\n\u003cp\u003e2*. Dr. Ebrahim Safari, Associate Professor of Physics, Department of Optics and Laser Physics, Faculty of Physics, University of Tabriz, Tabriz, Iran. Email address: [email protected]\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e3. Dr. Reza Safaralizadeh, Professor of Molecular Genetics, Department of Biology, Faculty of Natural Sciences, University of Tabriz, Tabriz, Iran. Email address: [email protected].\u003c/p\u003e\n\u003cp\u003e4. Dr. Gholamreza Dehghan, Professor of Biochemistry, Department of Biology, Faculty of Natural Sciences, University of Tabriz, Tabriz, Iran. Email address: [email protected].\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBray F et al (2024) Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. Cancer J Clin 74(3):229\u0026ndash;263\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOrganization WH (2024) Global cancer burden growing, amidst mounting need for services. 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Sensors 21(2):641\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbumazwed A et al (2016) Projection method for improving signal to noise ratio of localized surface plasmon resonance biosensors. Biomedical Opt Express 8(1):446\u0026ndash;459\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"plasmonics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"plas","sideBox":"Learn more about [Plasmonics](https://www.springer.com/journal/11468)","snPcode":"11468","submissionUrl":"https://submission.nature.com/new-submission/11468/3","title":"Plasmonics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Optical Biosensor, Cancer, Rapid Detection, Laser, Nanostructures, Goos-Hänchen Shift, Surface Plasmon Resonance","lastPublishedDoi":"10.21203/rs.3.rs-6737507/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6737507/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eEarly detection of cancer cells is crucial for effective disease management and personalized treatment. This study presents an advanced optical biosensor that integrates the Goos-H\u0026auml;nchen (GH) shift with Surface Plasmon Resonance (SPR) for highly sensitive, label-free and rapid cancer cell detection. The system consists of a red diode laser, beam splitter, polarizer, high-refractive index rotatable prism, and quadrant detector (QD) for precise lateral beam shift measurements. A differential configuration with control and test targets minimizes noise and enhances measurement accuracy. Lung (A549) and colon (LS180) cancer cells were cultured on nanoscale gold-coated glass substrates, interacting with the evanescent wave under total internal reflection (TIR). SPR analysis revealed resonance dip shifts of ~\u0026thinsp;1.6\u0026deg; for LS180 and ~\u0026thinsp;2.2\u0026deg; for A549 cells, while GH shift measurements further improved diagnostic precision, yielding lateral displacements of ~\u0026thinsp;5.8 \u0026micro;m for LS180 and ~\u0026thinsp;6.5 \u0026micro;m for A549. The sensor has a detection limit of ~\u0026thinsp;500,000 cells/cm\u0026sup2; and a refractive index sensitivity of 160\u0026deg;/RIU for LS180 and 220\u0026deg;/RIU for A549. With a limit of detection (LOD) of ~\u0026thinsp;6.8 \u0026times; 10⁻⁴ RIU and a figure of merit (FOM) of 106.7 (LS180) and 146.7 (A549), the system demonstrated high resolution and sensitivity. The dynamic range spans refractive indices from ~\u0026thinsp;1.33 to 1.37, enabling broad analyte detection. A signal-to-noise ratio (SNR) of 20:1 confirms robust signal reliability. By integrating the GH shift with SPR, this dual-mode biosensor significantly enhances sensitivity and accuracy, enabling rapid, non-invasive cancer cell detection. Its ability to distinguish between lung and colon cancer cells marks a valuable advancement in clinical diagnostics, supporting early detection and personalized medicine.\u003c/p\u003e","manuscriptTitle":"Development of an Optical Biosensor Based on the Goos-Hänchen Shift and Surface Plasmon Resonance for Rapid Detection of Cancer Cells","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-09 06:00:35","doi":"10.21203/rs.3.rs-6737507/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-06-16T14:22:58+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-16T13:26:08+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-09T10:32:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"178472359462979040906546516181540196610","date":"2025-06-07T11:45:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"205351178477274263094052304609918538824","date":"2025-06-06T08:48:00+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-05T11:40:08+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-28T07:36:11+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-28T07:36:05+00:00","index":"","fulltext":""},{"type":"submitted","content":"Plasmonics","date":"2025-05-24T07:35:07+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"plasmonics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"plas","sideBox":"Learn more about [Plasmonics](https://www.springer.com/journal/11468)","snPcode":"11468","submissionUrl":"https://submission.nature.com/new-submission/11468/3","title":"Plasmonics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"a6bec32f-472a-406a-b6c4-ba763bc89e47","owner":[],"postedDate":"June 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-07-28T16:05:07+00:00","versionOfRecord":{"articleIdentity":"rs-6737507","link":"https://doi.org/10.1007/s11468-025-03202-1","journal":{"identity":"plasmonics","isVorOnly":false,"title":"Plasmonics"},"publishedOn":"2025-07-26 15:57:22","publishedOnDateReadable":"July 26th, 2025"},"versionCreatedAt":"2025-06-09 06:00:35","video":"","vorDoi":"10.1007/s11468-025-03202-1","vorDoiUrl":"https://doi.org/10.1007/s11468-025-03202-1","workflowStages":[]},"version":"v1","identity":"rs-6737507","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6737507","identity":"rs-6737507","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}