Composition Distribution of the Thermal Soluble Organics from Naomaohu Lignite and Structural Characteristics of the Corresponding Insoluble Portions.

With cyclohexane (CH), benzene (BE), and ethyl acetate (EA) as solvents, Naomaohu lignite (NL, a typical oil-rich, low-rank coal) from Hami, Xinjiang, was thermally dissolved (TD) to obtain three types of soluble organics (NLCH, NLBE, and NLEA) and the corresponding insoluble portions (NLCH-R, NLBE-R, and NLEA-R). Ultimate analysis, X-ray photoelectron spectroscopy (XPS), Fourier transform infrared spectroscopy (FTIR), thermogravimetric analysis (TG-DTG), and gas chromatography-mass spectrometry (GC/MS) were used to characterize NL and its soluble and insoluble portions. Results showed that, compared with NL, the C element in NLCH-R, NLBE-R, and NLEA-R increased, while the O element decreased significantly, indicating that thermal dissolution is a carbon enrichment process and an effective deoxidation method. The GC/MS results showed that oxygen-containing organic compounds (OCOCs) are dominant in NLCH, NLBE, and NLEA. NLCH is mainly composed of ketones (11.90%) and esters (19.04%), while NLBE and NLEA are composed of alcohols (12.18% and 2.42%, respectively) and esters (66.09% and 84.08%, respectively), with alkyl and aromatic acid esters as the main components. Among them, EA exhibits significant selective destruction for oxygen-containing functional groups in NL. XPS, FTIR, and TG-DTG results showed that thermal dissolution can not only affect the macromolecular network structure of NL, but also improve its pyrolysis reactivity. In short, thermal dissolution can effectively obtain oxygen-containing organic compounds from NL.

Insight into molecular characteristics of a Chinese coal via separation, characterization, and data processing.

Dayan lignite was subjected to thermal dissolution sequentially with cyclohexane, acetone, and methanol. Each thermal dissolution extract was subjected to further separation/enrichment using column chromatography, which was sequentially eluted with petroleum ether, a mixture of ethyl acetate and petroleum ether (vol:vol = 1:1), and ethyl acetate. The three thermal dissolution extracts and nine enrichment subfractions were characterized by an Orbitrap mass spectrometry equipped with an atmospheric pressure chemical ionization ion source. The mass spectrometry data were also statistically analyzed by principal component analysis, which can reduce the dimensionality of data and classify multiple samples according to principal components. Identified compounds in the extracts and subfractions are classified into eight classes according to the heteroatom distribution. Hydrocarbon class is mainly presented in the petroleum ether fraction, and oxygen class, nitrogen class, and oxygen-nitrogen class are distributed in both petroleum ether/ethyl acetate and ethyl acetate subfractions. The combination of different analytical methods enhances the understanding of coal at the molecular level and provides important data for downstream refining processes.

Yield prediction of "Thermal-dissolution based carbon enrichment" treatment on biomass wastes through coupled model of artificial neural network and AdaBoost.

The "Thermal-dissolution based carbon enrichment" was proven as an efficient and homogenizing treatment method in converting biomass wastes into similar high-quality carbon materials. However, their yields varied significantly with respect to the different experimental parameters employed. It is therefore imperative to establish the correlation between product yield and experimental parameters for material selection and condition optimization. In this study, Adaboost was coupled with an artificial neural network algorithm to precisely describe the abovementioned correlation. The results demonstrated the effectiveness of this model through its outstanding predicting performance for all the products, especially, the coefficient of determination in predicting the yield of Residue was as high as 0.97. Additionally, the coupling effect of temperature and time was observed. This study not only validates a close correlation between selected experimental parameters and product yields, but also provides a quick and reliable way for material selection and condition optimization.

Ligand-Induced Stability of Gold Nanoclusters: Thiolate versus Selenolate.

Thiolate-protected gold nanoclusters have attracted considerable attention as building blocks for new functional materials and have been extensively researched. Some studies have reported that changing the ligand of these gold nanoclusters from thiolate to selenolate increases cluster stability. To confirm this, in this study, we compare the stabilities of precisely synthesized [Au25(SC8H17)18](-) and [Au25(SeC8H17)18](-) against degradation in solution, thermal dissolution, and laser fragmentation. The results demonstrate that changing the ligand from thiolate to selenolate increases cluster stability in reactions involving dissociation of the gold-ligand bond but reduces cluster stability in reactions involving intramolecular dissociation of the ligand. These results reveal that using selenolate ligands makes it possible to produce gold clusters that are more stable against degradation in solution than thiolate-protected gold nanoclusters.