Analysis of the surface plasmon resonance interferometric imaging performance of scanning confocal surface plasmon microscopy

Recent advances in surface plasmon resonance imaging and biological applications

Surface Plasmon Resonance Imaging (SPRI) is a robust technique for visualizing refractive index changes, which enables researchers to observe interactions between nanoscale objects in an imaging manner. In the past period, scholars have been attracted by the Prism-Coupled and Non-prism Coupled configurations of SPRI and have published numerous experimental results. This review describes the principle of SPRI and discusses recent developments in Prism-Coupled and Non-prism Coupled SPRI techniques in detail, respectively. And then, major advances in biological applications of SPRI are reviewed, including four sub-fields (cells, viruses, bacteria, exosomes, and biomolecules). The purpose is to briefly summarize the recent advances of SPRI and provide an outlook on the development of SPRI in various fields.

Differential confocal over-range determination method based on an information theory

The existing differential confocal axial three-dimensional (3D) measurement method cannot determine whether the surface height of the sample in the field of view is within its effective measurement range. Therefore, in this paper, we propose a differential confocal over-range determination method (IT-ORDM) based on an information theory to determine whether the surface height information of the sample to be examined is within the effective measurement range of the differential confocal axial measurement. First, the IT-ORDM finds the boundary position of the axial effective measurement range by the differential confocal axial light intensity response curve. Then the effective intensity measurement ranges of the pre-focus axial response curve (ARC) and the post-focus ARC are determined by the correspondence between the boundary position and the ARC. Finally, the intersection operation of the pre-focus image of effective measurement and the post-focus image of effective measurement is used to realize the extraction of the effective measurement area of the differential confocal image. The experimental results show that the IT-ORDM can effectively determine and restore the 3D shape of the measured sample surface at the reference plane position in the multi-stage sample experiments.

Detection of Heavy Metal Ions by Ratiometric Photoelectric Sensor

In recent years, heavy metal pollution has become increasingly serious. Heavy metals exist in an environment mainly in the form of ions (heavy metal ions, HMs). They can contaminate food, water, soil, and the atmosphere, leading to serious harm to plants and animals. With high bioavailability and nonbiodegradability, HMs can accumulate through biomagnification. Consequently, heavy metal pollution has become the cause of many fatal diseases threatening human health and ecological environment. Therefore, the accurate detection of HMs is vital and necessary. In this paper, the harm and limit standards of heavy metals were systematically summarized and the common analysis methods were overviewed and compared. Specifically, the latest research progress of ratiometric photoelectric sensor, including optical and electrical sensor, were mainly described. The research status and advantages and disadvantages of a photoelectric sensor were summarized. Furthermore, the future directions were proposed, which provided the reference for the further research and application of the ratiometric photoelectric sensor.

Analysis of Deep Learning-Based Phase Retrieval Algorithm Performance for Quantitative Phase Imaging Microscopy

Quantitative phase imaging has been of interest to the science and engineering community and has been applied in multiple research fields and applications. Recently, the data-driven approach of artificial intelligence has been utilized in several optical applications, including phase retrieval. However, phase images recovered from artificial intelligence are questionable in their correctness and reliability. Here, we propose a theoretical framework to analyze and quantify the performance of a deep learning-based phase retrieval algorithm for quantitative phase imaging microscopy by comparing recovered phase images to their theoretical phase profile in terms of their correctness. This study has employed both lossless and lossy samples, including uniform plasmonic gold sensors and dielectric layer samples; the plasmonic samples are lossy, whereas the dielectric layers are lossless. The uniform samples enable us to quantify the theoretical phase since they are established and well understood. In addition, a context aggregation network has been employed to demonstrate the phase image regression. Several imaging planes have been simulated serving as input and the label for network training, including a back focal plane image, an image at the image plane, and images when the microscope sample is axially defocused. The back focal plane image plays an essential role in phase retrieval for the plasmonic samples, whereas the dielectric layer requires both image plane and back focal plane information to retrieve the phase profile correctly. Here, we demonstrate that phase images recovered using deep learning can be robust and reliable depending on the sample and the input to the deep learning.