Ionic Referencing in Surface Plasmon Microscopy: Visualization of the Difference in Surface Properties of Patterned Monomolecular Layers

Deep Learning Approach for the Localization and Analysis of Surface Plasmon Scattering

Surface plasmon resonance microscopy (SPRM) combines the principles of traditional microscopy with the versatility of surface plasmons to develop label-free imaging methods. This paper describes a proof-of-principles approach based on deep learning that utilized the Y-Net convolutional neural network model to improve the detection and analysis methodology of SPRM. A machine-learning based image analysis technique was used to provide a method for the one-shot analysis of SPRM images to estimate scattering parameters such as the scatterer location. The method was assessed by applying the approach to SPRM images and reconstructing an image from the network output for comparison with the original image. The results showed that deep learning can localize scatterers and predict other variables of scattering objects with high accuracy in a noisy environment. The results also confirmed that with a larger field of view, deep learning can be used to improve traditional SPRM such that it localizes and produces scatterer characteristics in one shot, considerably increasing the detection capabilities of SPRM.

Deep Learning Approach for Enhanced Detection of Surface Plasmon Scattering

A deep learning approach has been taken to improve detection characteristics of surface plasmon microscopy (SPM) of light scattering. Deep learning based on the convolutional neural network algorithm was used to estimate the effect of scattering parameters, mainly the number of scatterers. The improvement was assessed on a quantitative basis by applying the approach to SPM images formed by coherent interference of scatterers. It was found that deep learning significantly improves the accuracy over conventional detection: the enhancement in the accuracy was shown to be significantly higher by almost 6 times and useful for scattering by polydisperse mixtures. This suggests that deep learning can be used to find scattering objects effectively in the noisy environment. Furthermore, deep learning can be extended directly to label-free molecular detection assays and provide considerably improved detection in imaging and microscopy techniques.

The Role of Anion Adsorption in the Effect of Electrode Potential on Surface Plasmon Resonance Response

Surface plasmon resonance, being widely used in bioanalytics and biotechnology, is influenced by the electrical potential of the resonant gold layer. To evaluate the mechanism of this effect, we have studied it in solutions of various inorganic electrolytes. The magnitude of the effect decreases according to the series: KBr>KCl>KF>NaClO₄ . The data were treated by using different models of the interface. A quantitative description was obtained for the model, which takes into account the local dielectric function of gold being affected by the free electron charge, diffuse ionic layer near the gold/water interface, and specific adsorption of halides to the gold surface with partial charge transfer. Taking into account that most biological experiments are performed in chloride-containing solutions, detailed analysis of the model at these conditions was performed. The results indicate that the chloride adsorption is the main mechanism for the influence of potential on the surface plasmon resonance. The dependencies of surface concentration and residual charge of chloride on the applied potential were determined.