The catalytic hydrogasification of char to synthetic natural gas was carried out in a pressurized fixed-bed reactor over Cu−Ni−Ca composite catalysts. The effect of the Cu−Ni ratio, Cu−Ni loading, reaction temperature on methane yield, and the methane formation rate was investigated. The characterization of these catalysts by X-ray diffraction showed that the Cu−Ni alloy was formed with a suitable Cu−Ni ratio during hydrogasification. Experiment results indicated that a suitable Cu− Ni ratio of 1:1 was favored for the formation of Cu−Ni alloy, which presented the highest catalytic activity. Methane yield and methane formation rate for 2.5%Cu−2.5%Ni−1.0%Ca reached 88.3% and 11.56 mL/(min•g char) at 800 °C and 2 MPa H 2 pressure in 450 min, respectively. The high catalytic performance of the Cu−Ni alloy was due to the isolation of Cu and Ni atoms from their adjacent atoms, resulting in more adsorption and dissociation of hydrogen molecules as well as the interaction between metal atoms and carbon. Moreover, a possible reaction mechanism for coal/char catalytic hydrogasification over Cu− Ni−Ca composite catalysts was proposed.

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Background: Rhabdomyosarcoma (RMS) is a malignant soft-tissue tumour. In recent years, the tumour microenvironment (TME) has been reported to be associated with the development of tumours. However, the relationship between the occurrence and development of RMS and TME is unclear. The purpose of this study is to identify potential tumor microenvironment-related biomarkers in rhabdomyosarcoma and analyze their molecular mechanisms, diagnostic and prognostic significance.Methods: We first applied bioinformatics method to analyse the tumour samples of 187 patients with rhabdomyosarcoma (RMS) from the Gene Expression Omnibus database (GEO). Then, we used cell function and molecular biology techniques to study the progress of RMS.Results: Bioinformatics results show that the RMS TME key genes were screened, and a TME-related tumour clinical staging model was constructed. The top 10 hub genes were screened through the establishment of a protein-protein interaction network, and then Gene Expression Profiling Interactive Analysis was conducted to measure the overall survival (OS) of the 10 hub genes in the sarcoma cases in The Cancer Genome Atlas (TCGA). Six differential genes of statistical significance were acquired. The correlation between these six differential genes and the clinical stage of RMS was analysed. Our data found that the expression levels of MAD2L1 and CCNB2 negatively correlated with the OS of RMS patients and positively correlated with the clinical stage of RMS patients. Immunohistochemical results also confirmed that the expression levels of MAD2L1 (30/33, 87.5%) and CCNB2 (33/33, 100%) were remarkably higher in RMS group than in normal control group (0/11, 0%). Moreover, the expression of CCNB2 was related to tumour size. Further gene set enrichment analysis revealed that the genes in MAD2L1 and CCNB2 groups with high expression were mainly related to the mechanism of tumour metastasis and recurrence. In the low-expression MAD2L1 and CCNB2 groups, the genes were enriched in the metabolic and immune pathways. Downregulation of MAD2L1 and CCNB2 suppressed the growth, invasion, migration, and cell cycling of RMS cells and promoted their apoptosis. The CIBERSORT immune cell fraction analysis indicated that the expression levels of MAD2L1 and CCNB2 affected the immune status in the TME. Conclusions: The expression levels of MAD2L1 and CCNB2 may help guide the prognosis of patients with RMS and the clinical staging of tumours.