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In this study, a novel non-metallic carbon-based catalyst co-doped with boron and nitrogen (B,N) was successfully synthesized. By precisely controlling the carbonization temperature of a binary mixed ionic liquid, we selectively modified the doping site structure, ultimately constructing a B,N co-doped frustrated Lewis acid-base pair catalyst. This catalyst exhibited remarkable catalytic activity, selectivity, and stability in the dehydrochlorination reaction of 1,1,2-trichloroethane (TCE). Detailed characterization and theoretical calculations revealed that the primary active center of this catalyst was the BN3 configuration. Compared to conventional graphitic N structures, the BN3 structure had a higher p-band center, ensuring superior adsorption and activation capabilities for TCE during the reaction. Within the BN3 site, three negatively charged nitrogen atoms acted as Lewis bases, while positively charged boron atoms acted as Lewis acids. This synergistic interaction facilitated the specific dissociation of chlorine and hydrogen atoms from TCE, significantly enhancing the 1,1-dichloroethene selectivity. Through this research, we not only explored the active site structure and catalytic mechanism of B,N co-doped catalysts in depth but also provided an efficient, selective, and stable catalyst for the dehydrochlorination of TCE, contributing significantly to the development of nonmetallic catalysts. [ABSTRACT FROM AUTHOR]

BACKGROUND: Although cone beam computed tomography (CBCT) plays an important role in the diagnosis and treatment of oral diseases, its image segmentation method needs to be further improved, and there are still objections about the clinical application effect of general anesthesia (GA) on children's dental fear (CDF). OBJECTIVE: This study aimed to investigate the application value of CBCT based on intelligent computer segmentation model in oral diagnosis and treatment of children in the context of biomedical signals, and to analyze the alleviating effect of GA on CDF. METHODS: Based on the regional level set (CV) algorithm, the local binary fitting (LBF) model was introduced to optimize it, and the tooth CBCT image segmentation model CV-LBF was established to compare the segmentation accuracy (SA), maximum symmetric surface distance (MSSD), average symmetric surface distance (ASSD), over segmentation rate (OR), and under segmentation rate (UR) between these model and other algorithms. 82 children with CDF were divided into general anesthesia group (GAG) (n = 38) and controls (n = 44) according to the voluntary principle of their families. Children in GAG were treated with GA and controls with protective fixed intervention. Children's fear survey schedule-dental subscale (CFSS-DS) and Venham scores were counted before intervention in the two groups. CFSS-DS scores were recorded at 2 hours after intervention and after recovery in children in GAG. CFSS-DS and Venham scores were performed in all children 1 week after surgery. RESULTS: The results showed that the S ⁢ A value of CV-LBF algorithm was higher than that of region growing algorithm (P < 0.05). OR, UR, MSSD, and ASSD values of CV-LBF algorithm were evidently lower than those of other algorithms (P < 0.05). CFSS-DS scores were lower in GAG than in controls 2 hours after intervention and at return visits after 1 week of intervention (P < 0.001), and Venham scores were lower in GAG than in controls after intervention (P < 0.001). After intervention, the proportion of children with Venham grade 0, 1, 2, and 3 was obviously higher in GAG than in controls (P < 0.001), while the proportion of children with Venham grade 4 and 5 was clearly higher in controls than in GAG (P < 0.001). CONCLUSION: The results revealed that the computer intelligent segmentation model CV-LBF has potential application value in CBCT image segmentation of children's teeth, and GA can effectively alleviate anxiety of children with CDF and can be used as biomedical signals. [ABSTRACT FROM AUTHOR]