Multi-response optimization of chromium/gold-based nanofilm Kretschmann-based surface plasmon resonance glucose sensor using finite-difference time-domain and Taguchi method

Abstract

Kretschmann-based surface plasmon resonance sensor utilizing chromium and gold nanofilms is ideal for label-free biomedical sensing. In this work, Taguchi’s L9 orthogonal array method was used to optimize the effects of three control factors and noise factor, which are the incident optical wavelength, chromium and gold nanofilm thicknesses, and their root-mean-square surface roughness, on the performance of the Kretschmann-based surface plasmon resonance sensor. The control factors were varied at three levels for a novel multi-response optimization of the Kretschmann-based surface plasmon resonance sensor for the minimum reflectivity, the full-width-at-half-maximum, and the sensitivity of 3% glucose detection, executed using Lumerical’s two-dimensional finite-difference time-domain method. Using Taguchi method, the best control factor setting in air was A3B2C2 corresponding to 785 nm optical wavelength, 0.5 nm chromium, and 50 nm gold layer thickness, respectively, with minimum reflectivity of 0.0017%, full-width-at-half-maximum of 0.4759°, and glucose-sensing sensitivity of 106.73°·RIU−1. The detection accuracy and quality factor were 0.01 and 224.26 RIU−1, respectively. It was also indicated that chromium nanofilm thickness of 0.5–3 nm and its root-mean-square surface roughness has a negligible factor effect compared to other control factors. Taguchi method’s factor effect analysis showed that for chromium layer thickness of 1–3 nm, the minimum reflectivity values are predominantly determined by the gold layer thickness with 75% factor effect, followed by optical wavelength with 11%. Factor effect of full-width-at-half-maximum is determined by optical wavelength (57%), followed by gold layer thickness (38%). Sensitivity is 88% determined by optical wavelength and 10% determined by gold layer thickness. The Kretschmann-based surface plasmon resonance glucose sensor with the best glucose-sensing sensitivity was at optical wavelength of 632.8 nm with a higher sensitivity value of 163.415°·RIU−1 but lower detection accuracy and quality factor values of 0.001 and 24.86 RIU−1, respectively, compared to near-infrared wavelength of 785 nm. In conclusion, finite-difference time-domain and Taguchi method is suitable for multi-response optimization of control and noise factors of Kretschmann-based surface plasmon resonance sensors.

Visible and angular interrogation of Kretschmann-based SPR using hybrid Au–ZnO optical sensor for hyperuricemia detection

“…For the materials database, the refractive index of BK7, Au and Cr are taken from BioNavis [ 26 , 42 ] and the refractive index of ZnO is obtained from Stelling [ 26 , 43 ]. Meanwhile the RI of uric acid at different concentrations of 0 mM–3.0 mM were obtained from Gan et al [ 11 ] with RI of 1.333, 1.3331, 1.3332, 1.3334, 1.3335, and 1.3336, respectively.…”

Surface Plasmon Resonance Biosensors Based on Kretschmann Configuration: Basic Instrumentation and Applications

“…Another bimetallic Ag (30nm)/Au (17.5nm) thin film based SPR glucose sensor was reported by Mulyanti et al [158] that showed sensitivity and figure of merit values of 4.7993°/RIU and 0.5173 at 633 nm incident wavelength for a glucose concentration of 0.08 g/dl. Most recently, other multi-layer, e.g., chromium/gold [159], Ag/Si3N4/Au [160], and SiO2 /gold/silver [161] thin film based SPR biosensors have shown excellent performances in the detection of glucose in blood or glucose/water solutions. Notably, different methods, including Taguchi method, FDTD simulation, COMSOL simulation, were used in such works to optimize the sensor design considerations.…”

Multi-Response Optimisation of Automotive Door Using Grey Relational Analysis with Entropy Weights

“…The Taguchi method is used as an efficient and frugal design method that is scientific and practical in exploring the optimal state. It is a special design method which reduces the number of tests by utilizing mathematical fundamentals [ 13 , 14 , 15 ]. Liu et al [ 16 ] established a multi-body dynamic model of a suspended monorail vehicle.…”