Development and Investigation of a Dual-Pad In-Channel Referencing Surface Plasmon Resonance Sensor

Herein, we describe the construction of a novel dual-pad referencing surface plasmon resonance (SPR) sensor utilizing electrolytic grafting of diazonium salts to individually functionalize two gold pads positioned in a single fluidic channel. Using a dove prism, a simple single axis optical train may be employed without compromising the analytical performance. Once functionalized, one pad is used as the analytical sensing pad for detection of molecular interactions while the other serves as the reference pad, compensating for background refractive index fluctuations. The reference pad effectively compensates bulk refractive index changes and temperature variations as well as other nonspecific effects. The sensor was applied to calibration of a pH-responsive polymer layer in the presence of bulk refractive index and temperature variations. Monitoring selective attachment of a protein is also demonstrated. To our knowledge, this is the first implementation of in-channel referencing SPR sensor utilizing diazonium salt-based surface chemistry

Adsorbate–Metal Bond Effect on Empirical Determination of Surface Plasmon Penetration Depth

The penetration depth of surface plasmons is commonly determined empirically from the observed response for adsorbate loading on gold surface plasmon resonance (SPR) substrates. However, changes in the SPR spectrum may originate from both changes in the effective refractive index near the metal surface and changes in the metal permittivity following covalent binding of the adsorbate layer. Herein, the significance of incorporating an additional adsorbate–metal bonding effect in the calculation is demonstrated in theory and in practice. The bonding effect is determined from the nonzero intercept of a SPR shift versus adsorbate thickness calibration and incorporated into the calculation of penetration depth at various excitation wavelengths. Determinations of plasmon penetration depth with and without the bonding response for alkanethiolate–gold are compared and are shown to be significantly different for a thiol monolayer adsorbate system. Additionally, plasmon penetration depth evaluated with bonding effect compensation shows greater consistency over different adsorbate thicknesses and better agreement with theory derived from Maxwell’s equation, particularly for adsorbate thicknesses that are much smaller (<5%) than the plasmon penetration depth. The method is also extended to a more practically applicable polyelectrolyte multilayer adsorbate system

Sensing with Prism-Based Near-Infrared Surface Plasmon Resonance Spectroscopy on Nanohole Array Platforms

Nanohole arrays exhibit unique surface plasmon resonance (SPR) characteristics according to hole periodicity, diameter, and excitation wavelength (λ_SPR). This contribution investigates the SPR characteristics and surface sensitivity of various nanohole arrays with the aim of tuning the parameters for optimal sensing capability. Both the Bragg surface plasmons (SPs) arising from diffraction by the periodic holes and the traditional propagating SPs are characterized with emphasis on sensing capability of the propagating SPs. Several trends in bulk sensitivity and penetration depth were established, and the surface sensitivity was calculated from bulk sensitivity and penetration depth of the SPs for different analyte thicknesses. Increased accuracy and precision in penetration depth values were achieved by incorporating adsorbate effects on substrate permittivity. The optimal nanohole array conditions for highest surface sensitivity were determined (820 nm periodicity, 0.27 diameter/periodicity, and λ_SPR = 1550 nm), which demonstrated an increase in surface sensitivity for the 10 nm analyte over continuous gold films at their optimal λ_SPR (1300 nm) and conventional visible λ_SPR (700 nm)

Electrografted Diazonium Salt Layers for Antifouling on the Surface of Surface Plasmon Resonance Biosensors

Electrografted diazonium salt layers on the surface of surface plasmon resonance (SPR) sensors present potential for a significant improvement in antifouling coatings. A pulsed potential deposition profile was used in order to circumvent mass-transport limitations for layer deposition rate. The influence of number of pulses with respect to antifouling efficacy was evaluated by nonspecific adsorption surface coverage of crude bovine serum proteins. Instead of using empirical and rough estimated values, the penetration depth and sensitivity of the SPR instrument were experimentally determined for the calculation of nonspecific adsorption surface coverage. This provides a method to better examine antifouling surface coatings and compare crossing different coatings and experimental systems. Direct comparison of antifouling performance of different diazonium salts was facilitated by a tripad SPR sensor design. The electrografted 4-phenylalanine diazonium chloride (4-APhe) layers with zwitterionic characteristic demonstrate ultralow fouling

Improving Prediction of Peroxide Value of Edible Oils Using Regularized Regression Models

We present four unique prediction techniques, combined with multiple data pre-processing methods, utilizing a wide range of both oil types and oil peroxide values (PV) as well as incorporating natural aging for peroxide creation. Samples were PV assayed using a standard starch titration method, AOCS Method Cd 8-53, and used as a verified reference method for PV determination. Near-infrared (NIR) spectra were collected from each sample in two unique optical pathlengths (OPLs), 2 and 24 mm, then fused into a third distinct set. All three sets were used in partial least squares (PLS) regression, ridge regression, LASSO regression, and elastic net regression model calculation. While no individual regression model was established as the best, global models for each regression type and pre-processing method show good agreement between all regression types when performed in their optimal scenarios. Furthermore, small spectral window size boxcar averaging shows prediction accuracy improvements for edible oil PVs. Best-performing models for each regression type are: PLS regression, 25 point boxcar window fused OPL spectral information RMSEP = 2.50; ridge regression, 5 point boxcar window, 24 mm OPL, RMSEP = 2.20; LASSO raw spectral information, 24 mm OPL, RMSEP = 1.80; and elastic net, 10 point boxcar window, 24 mm OPL, RMSEP = 1.91. The results show promising advancements in the development of a full global model for PV determination of edible oils