RESUMO
The regulation of hollow morphology, band structure modulation of solid solution, and introduction of cocatalysts greatly promote the separation of electron-hole pairs in photocatalytic processes, which is of great significance for the process of photocatalytic hydrogen evolution (PHE). In this study, we constructed Zn1-xCdxS hollow solid solution photocatalysts using template and ion exchange methods, and successfully loaded PdS quantum dots (PdS QDs) onto the solid solution through in situ sulfidation. Significantly, the 0.5 wt% PdS QDs/Zn0.6Cd0.4S composite material achieved a H2 production rate of 27.63 mmol g-1 h-1 in the PHE process. The hollow structure of the composite material enhances processes such as light reflection and scattering, the band structure modulation of the solid solution enables the electron-hole pairs to reach an optimal exciton recombination balance, and the modification of PdS QDs provides abundant sites for oxidation, thereby promoting the proton reduction and hydrogen evolution rate. This work provides valuable guidance for the rational design of efficient composite PHE catalysts with strong internal electric field.
RESUMO
BACKGROUND: During the biomass-to-bio-oil conversion process, many studies focus on studying the association between biomass and bio-products using near-infrared spectra (NIR) and chemical analysis methods. However, the characterization of biomass pyrolysis behaviors using thermogravimetric analysis (TGA) with support vector machine (SVM) algorithm has not been reported. In this study, tobacco was chosen as the object for biomass, because the cigarette smoke (including water, tar, and gases) released by tobacco pyrolysis reactions decides the sensory quality, which is similar to biomass as a renewable resource through the pyrolysis process. RESULTS: SVM algorithm has been employed to automatically classify the planting area and growing position of tobacco leaves using thermogravimetric analysis data as the information source for the first time. Eighty-eight single-grade tobacco samples belonging to four grades and eight categories were split into the training, validation, and blind testing sets. Our model showed excellent performances in both the training and validation set as well as in the blind test, with accuracy over 91.67%. Throughout the whole dataset of 88 samples, our model not only provides precise results on the planting area of tobacco leave, but also accurately distinguishes the major grades among the upper, lower, and middle positions. The error only occurs in the classification of subgrades of the middle position. CONCLUSIONS: From the case study of tobacco, our results validated the feasibility of using TGA with SVM algorithm as an objective and fast method for auto-classification of tobacco planting area and growing position. In view of the high similarity between tobacco and other biomasses in the compositions and pyrolysis behaviors, this new protocol, which couples the TGA data with SVM algorithm, can potentially be extrapolated to the auto-classification of other biomass types.
