Boosting Catalytic and Anti-fluorination Performance of the Ru/Vanadia-Titania Catalyst for the Oxidative Destruction of Freon by Sulfuric Acid Modification.

Chlorofluorocarbons (CFCs) exert a strong greenhouse effect and constitute the largest contributor to ozone depletion. Catalytic removal is considered an effective pathway for eliminating low-concentration CFCs under mild conditions. The key issue is the easy deactivation of the catalysts due to their surface fluorination. We herein report a comparative investigation on catalytic dichlorodifluoromethane (CFC-12) removal in the absence or presence of water over the sulfuric-acid-modified three-dimensionally ordered macroporous vanadia-titania-supported Ru (S-Ru/3DOM VTO) catalysts. The S-Ru/3DOM VTO catalyst exhibited high activity (T90% = 278 °C at space velocity = 40 000 mL g-1 h-1) and good stability within 60 h of on-stream reaction in the presence of 1800 ppm of water due to the improvements in acid site amount and redox ability that promoted the adsorption of CFC-12 and the activation of C-F bonds. Compared with the case under dry conditions, catalytic performance for CFC-12 removal was better over the S-Ru/3DOM VTO catalyst in the presence of water. Water introduction mitigated surface fluorination by the replenishment of hydroxyl groups, inhibited the formation of halogenated byproducts via the surface fluorine species cleaning effect, and promoted the reaction pathway of COX2 (X = Cl/F) → carboxylic acid → CO2.

A Multicenter Clinical Study of the Automated Fundus Screening Algorithm.

Purpose: To evaluate the effectiveness of automated fundus screening software in detecting eye diseases by comparing the reported results against those given by human experts. Results: There were 1585 subjects who completed the procedure and yielded qualified images. The prevalence of referable diabetic retinopathy (RDR), glaucoma suspect (GCS), and referable macular diseases (RMD) were 20.4%, 23.2%, and 49.0%, respectively. The overall sensitivity values for RDR, GCS, and RMD diagnosis are 0.948 (95% confidence interval [CI], 0.918-0.967), 0.891 (95% CI, 0.855-0.919), and 0.901 (95% CI-0.878, 0.920), respectively. The overall specificity values for RDR, GCS, and RMD diagnosis are 0.954 (95% CI, 0.915-0.965), 0.993 (95% CI-0.986, 0.996), and 0.955 (95% CI-0.939, 0.968), respectively. Methods: We prospectively enrolled 1743 subjects at seven hospitals throughout China. At each hospital, an operator records the subjects' information, takes fundus images, and submits the images to the Image Reading Center of Zhongshan Ophthalmic Center, Sun Yat-Sen University (IRC). The IRC grades the images according to the study protocol. Meanwhile, these images will also be automatically screened by the artificial intelligence algorithm. Then, the analysis results of automated screening algorithm are compared against the grading results of IRC. The end point goals are lower bounds of 95% CI of sensitivity values that are greater than 0.85 for all three target diseases, and lower bounds of 95% CI of specificity values that are greater than 0.90 for RDR and 0.85 for GCS and RMD. Conclusions: Automated fundus screening software demonstrated a high sensitivity and specificity in detecting RDR, GCS, and RMD from color fundus imaged captured using various cameras. Translational Relevance: These findings suggest that automated software can improve the screening effectiveness for eye diseases, especially in a primary care context, where experienced ophthalmologists are scarce.