Efficient degradation of organic pollutants by S-NaTaO3/biochar under visible light and the photocatalytic performance of a permonosulfate-based dual-effect catalytic system.

Removing large concentrations of organic pollutants from water efficiently and quickly under visible light is essential to developing photocatalytic technology and improving solar energy efficiency. This study used a simple hydrothermal method to prepare a non-metallic, S-doped NaTaO3 (S-NTO) photocatalyst, which was then loaded onto biochar (BC) to form a S-NTO/BC composite photocatalyst. After uniform loading onto BC, the S-NTO particles transformed from cubic to spherical. The photogenerated electron-hole pair recombination probability of the composite photocatalyst was significantly lower than those of the NTO particles. The light absorption range of the catalyst was effectively widened from 310 nm UV region to visible region. In addition, a dual-effect catalytic system was constructed by introducing peroxymonosulfate (PMS) into the environment of the pollution to be degraded. The Rhodamine B, Methyl Orange, Acid Orange 7, tetracycline, and ciprofloxacin degradation efficiency at 40 mg/L reached 99.6%, 99.2%, 84.5%, 67.1%, and 70.7%, respectively, after irradiation by a 40 W lamps for 90 min. The high-efficiency visible-light catalytic activity of the dual-effect catalytic system was attributed to doping with non-metallic sulfur and loading of catalysts onto BC. The development of this dual-effect catalytic system provides new ideas for quickly and efficiently solving the problem of high-concentration organic pollution in aqueous environments, rationally and fully utilizing solar energy, and expanding the application of photocatalytic technology to practice.

Analysis of the Association between Teachers' Classroom Teaching Behaviors and Students' Knowledge Acceptance Based on Psychological Data Analysis.

This paper adopts the method of psychological data analysis to conduct in-depth research and analysis on the correlation between teachers' classroom teaching behaviors and students' knowledge acceptance. Firstly, this paper proposes a health factor prediction model, which is specifically divided into clustering and then classification model and a clustering and classification synthesis model. The classroom learning process is coded, sampled, and quantified to obtain data on students' learning behaviors, and a visualization system based on classroom students' learning behaviors is designed and developed to record and analyze students' behaviors in the classroom learning process and grasp students' classroom learning. These two models use algorithms to fine-grained divide the dataset from the perspective of subject users and mental health factors, respectively, and then use decision tree algorithms to classify and predict the mental health factor information by the subject user base information. Second, based on the collected datasets, we designed comparison experiments to validate the clustering-then-classification model and the integrated clustering-classification model and selected the optimal model for comparison. Teachers should increase effective praise and encouragement behaviors; teachers should increase meaningful teacher-student interaction behaviors; teachers should be proficient in teaching media technology to reduce unnecessary time wastage. Strategies to enhance teachers' TPACK include enriching teachers' knowledge base of CK, TK, and PK; developing teachers' integration thinking; and enriching teachers' types of activities for integrating technology.