Accurately predicting CO 2 equilibrium solubility is essential for optimizing amine-based sorbents in carbon capture processes. However, traditional experimental approaches require extensive testing, making data collection time-consuming and often incomplete. In this study, we address this challenge by developing six machine learning models to predict CO 2 solubility in three dual-amine blends (MEA + MDEA, MEA + DEEA, and MEA + DMEA), significantly reducing the reliance on labor-intensive experiments. The PSO-BPNN model achieved the highest accuracy, with errors below 2.2 %, demonstrating the potential of machine learning to provide reliable solubility data without exhaustive experimental measurements. Additionally, we experimentally evaluated the CO 2 absorption and desorption performance of MEA-based blends, complemented by 13 C NMR spectroscopy to elucidate reaction mechanisms. Among the tested systems, the 2 M MEA + 3 M DEEA blend exhibited the highest CO 2 loading, fastest desorption rate, and lowest heat duty compared to 5 M MEA, underscoring its potential as a superior sorbent for energy-efficient CO 2 capture.
Fucosylated glycosaminoglycans (FGs) are complex glycosaminoglycans that exhibit potent anticoagulant activity. To study the relationship between molecular size and biological activity, oligosaccharides with (2,5)-anhydro- d -talose units at new reducing ends were prepared by hydrazine deacetylation and nitrous acid depolymerization. The product chemical structures were analyzed by one- and two-dimensional NMR methods. Additionally, anticoagulant activities were evaluated by clotting assay and chromogenic substrate cleavage. The results demonstrated that under mild deacetylation and deaminative cleavage conditions, both products were relatively homogeneous and sulfated fucose branch types and sulfate substituents remained stable. These depolymerized FGs with different molecular sizes had potent intrinsic anticoagulant activities, which were similar to those that were obtained by free-radical depolymerization with similar molecular weights. Decreasing molecular weight may weaken activity but not significantly affect factor Xase and heparin cofactor II (HCII)-mediated thrombin inhibition.
Sisheng Wu,Cuiyuan Jin,Yueyi Wang,Zhengwei Fu,Yuanxiang Jin
Propamocarb (PM) is a widely used fungicide with property of affecting fatty acid and phospholipid biosynthesis in funguses. In this study, we explored its effects on mice gut microbiota and metabolism by exposing mice to 3, 30, and 300 mg/L PM through drinking water for a duration of 28 days. We observed that the transcription of hepatic genes related to regulate lipid metabolism were perturbed by PM exposure. The microbiota in the cecal contents and feces changed during or after PM exposure at phylum or genus levels. 16S rRNA gene sequencing for the cecal content revealed shifted in overall microbial structure after PM exposure, and operational taxonomic unit (OTU) analysis indicated that 32.2% of OTUs changed by 300 mg/mL PM exposure for 28 days. In addition, based on 1 H NMR analysis,a total of 20 fecal metabolites mainly including succinate, short chain fatty acids, bile acids and trimethylamine were found to be significantly influenced by exposure to 300 mg/L PM.,. These metabolites were tightly correlated to host metabolism. Our findings indicated that high doses of PM exposure could disturb mice metabolism through, or partly through, altering the gut microbiota and microbial metabolites.
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