Electrodeposition of tin on Nafion-bonded carbon black as an active catalyst layer for efficient electroreduction of CO2 to formic acid

Abstract Electroreduction of CO2 to formic acid (ERCF) based on gas diffusion electrodes (GDEs) has been considered as a promising method to convert CO2 into value-added chemicals. However, current GDEs for ERCF suffer from low efficiency of electron transfer. In this work, a novel Sn-based gas diffusion electrode (ESGDE) is prepared by electrodepositing Sn on Nafion-bonded carbon black as catalyst layer to enhance electron transfer and thus the efficiency of ERCF. The highest Faraday efficiency (73.01 ± 3.42%), current density (34.21 ± 1.14 mA cm−2) and production rate (1772.81 ± 59.08 μmol m−2 s−1) of formic acid are obtained by using the ESGDE with electrodeposition time of 90 s in 0.5 M KHCO3 solution, which are one of the highest values obtained from Sn-based gas diffusion electrodes under similar conditions. The notable efficiency of ERCF achieved here should be attributed to the enhancement in the reactants transfer as well as the three-dimensional reaction zone. This work will be helpful for the industrial application of GDEs in EFCF

Building an ensemble learning model for gastric cancer cell line classification via rapid raman spectroscopy

Cell misuse and cross-contamination can affect the accuracy of cell research results and result in wasted time, manpower and material resources. Thus, cell line identification is important and necessary. At present, the commonly used cell line identification methods need cell staining and culturing. There is therefore a need to develop a new method for the rapid and automated identification of cell lines. Raman spectroscopy has become one of the emerging techniques in the field of microbial identification, with the advantages of being rapid and noninvasive and providing molecular information for biological samples, which is beneficial in the identification of cell lines. In this study, we built a library of Raman spectra for gastric mucosal epithelial cell lines GES-1 and gastric cancer cell lines, such as AGS, BGC-823, HGC-27, MKN-45, MKN-74 and SNU-16. Five spectral datasets were constructed using spectral data and included the full spectrum, fingerprint region, high-wavelength number region and Raman background of Raman spectra. A stacking ensemble learning model, SL-Raman, was built for different datasets, and gastric cancer cell identification was achieved. For the gastric cancer cells we studied, the differentiation accuracy of SL-Raman was 100% for one of the gastric cancer cells and 100% for six of the gastric cancer cells. Additionally, the separation accuracy for two gastric cancer cells with different degrees of differentiation was 100%. These results demonstrate that Raman spectroscopy combined with SL-Raman may be a new method for the rapid and accurate identification of gastric cancer. In addition, the accuracy of 94.38% for classifying Raman spectral background data using machine learning demonstrates that the Raman spectral background contains some useful spectral features. These data have been overlooked in previous studies