Raman spectrometer for the automated scan of large painted surfaces

Low power density, high-efficiency reflective Raman system for polymer SERS substrates

Surface-enhanced Raman spectroscopy (SERS) is a powerful measurement method in the chemical analysis field. It is much superior to bulk Raman owing to the enhancement of signal sensitivity from the SERS substrate. Nevertheless, the delicate SERS substrates are overpriced, which results in the difficulty of universal measurements. Accordingly, opting for a substrate made of polymer material based on the nanoimprint technique shows great potential for low-cost and high-performance SERS substrates. However, due to its low heat conductivity, the polymer's thermal properties may cause heat to concentrate on the incident spot and damage the nanostructures or analytes. In this article, we proposed a novel design of the Reflective Raman (RR) system to reduce the input power density and maintain high collection efficiency at the same time. The proposed RR system was directly compared with a traditional micro Raman (μ-Raman) system and demonstrated its outstanding performance for low damage threshold analytes and SERS substrates.

An autofocus algorithm considering wavelength changes for large scale microscopic hyperspectral pathological imaging system

Microscopic hyperspectral imaging technology has been widely used to acquire pathological information of tissue sections. Autofocus is one of the most important steps in microscopic hyperspectral imaging systems to capture large scale or even whole slide images of pathological slides with high quality and high speed. However, there are quite few autofocus algorithm put forward for the microscopic hyperspectral imaging system. Therefore, this article proposes a Laplace operator based autofocus algorithm for microscopic hyperspectral imaging system which takes the influence of wavelength changes into consideration. Through the proposed algorithm, the focal length for each wavelength can be adjusted automatically to ensure that each single band image can be autofocused precisely with adaptive image sharpness evaluation method. In addition, to increase the capture speed, the relationship of wavelength and focal length is derived and the focal offsets among different single band images are calculated for pre-focusing. We have employed the proposed method on our own datasets and the experimental results show that it can capture large-scale microscopic hyperspectral pathology images with high precise.