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This paper first investigates the main performance of the shackles of traditional physics teaching on students and its negative consequences and then designs a physics teaching process model based on the teaching environment of information big data and constructs a framework of the physics teaching system by utilizing data mining technology. Then, the physics teaching system is modeled based on the artificial neuron model and recurrent neural network, and the attention mechanism is used to improve the overall generalization performance of the model. Finally, the application effect of the physics teaching system is compared through empirical evidence. The results show that there is a deviation in the mean value of the two classes; the average score of the experimental class is 75.552 points, and the average score of the control class is 65.910 points, and there is a difference of 9.642 points between the two classes’ mean values. The data show that applying a physics teaching system can improve the learning status quo of students’ mechanical imitation, help students better master knowledge, enhance students’ ability to use information technology and achieve better teaching results than traditional teaching.

In today’s fast development of big data information technology, the traditional physics experiment method is difficult to adapt to the new curriculum reform physics experiment course objectives. The key topic of concern today is how to use big data information technology and physics experiment course integration for teaching. This study mainly uses the fuzzy comprehensive evaluation algorithm in big data information technology to study and analyze the physics laboratory courses in colleges and universities. Firstly, according to the principles and methods of the fuzzy comprehensive evaluation method, the model of a fuzzy comprehensive evaluation of university physics laboratory teaching is established, and the index system and evaluation level criteria of university physics laboratory teaching are established. Then the weights of each factor of the index system were determined using fuzzy comprehensive evaluation, and the evaluation results combining qualitative and quantitative were obtained by processing the data. Finally, the results were obtained regarding teaching attitude, teaching content, and teaching effect: the weights of good 55%, 64% and 38% performed better than other evaluation grades. Regarding teaching implementation: 54% weight of good evaluation is greater than other evaluation grades. From the evaluation grade, the average weight of a good grade is 48.25% greater than the other three categories, and the weight performance is better. This study is conducive to improving the experimental application ability of college students and thus has important historical significance for the development of physics in China.

In order to provide a way to develop the teaching effectiveness of physics experiments, this paper optimizes the platform search engine by combining heterogeneous data features and representing document information as feature vectors based on visual learning analysis methods. The algorithm is dynamically adjusted according to the authority to build a network database. And the virtual physics experiments have interacted with virtual experimental equipment to build a physics virtual simulation experiment platform. The results show that the overall level of visual student portrait analysis is above 40%, and the average completion efficiency of visual evaluation tasks 1-9 is 87.9%, which helps the digital transformation and upgrading of experimental physics teaching and promotes the construction of high-quality virtual simulation experimental teaching system.

Low crystalline photocatalysts with unsaturated active sites, such as oxygen vacancy (Ov), is reported to exhibit enhanced adsorption and activation of oxygen‐containing small molecules, such as H2O and CO2, thus boosting the activity in photocatalytic H2 evolution and CO2 reduction. However, numerous low‐crystalline photocatalysts show unsatisfactory stability due to the easily repaired surface Ov. Herein, three SnNb2O6 with different crystallinity were prepared by hydrothermal approach with similar precursors. Compared with bulk SnNb2O6 and ultra‐thin layered SnNb2O6, low‐crystalline SnNb2O6 (SNA) exhibits optimal visible‐light‐driven evolution rates of H2 (86.04 μmol g−1 h−1) and CO from CO2 (71.97 μmol g−1 h−1), which is mainly ascribed to the fast separation of the photogenerated carriers and enhanced photoreduction power caused by the surface Ov. More importantly, the sharp decrease of photocatalytic activity of SNA after seven cycles is well restored by the hydrothermal treatment of recycled SNA, ascribed to the reactivated surface Ov with the recovered low‐crystalline structure. These works thus offer a promising strategy for developing low‐crystalline and amorphous photocatalysts with high activity and stability. [ABSTRACT FROM AUTHOR]

The modulation of Z-scheme charge transfer is essential for efficient heterostructure toward photocatalytic CO 2 reduction. However, constructing a compact hetero-interface favoring the Z-scheme charge transfer remains a great challenge. In this work, an interfacial Nb-O-Sn bond and built-in electric field-modulated Z-scheme O v -SnO 2 /SnNb 2 O 6 heterojunction was prepared for efficient photocatalytic CO 2 conversion. Systematic investigations reveal that an atomic-level interface is constructed in the O v -SnO 2 /SnNb 2 O 6 heterojunction. Under simulated sunlight irradiation, the obtained O v -SnO 2 /SnNb 2 O 6 photocatalyst exhibits a high CO evolution rate of 147.4 μmol h -1 g -1 from CO 2 reduction, which is around 3-fold and 3.3-fold of SnO 2 /SnNb 2 O 6 composite and pristine SnNb 2 O 6 , respectively, and favorable cyclability by retaining 95.8% rate retention after five consecutive tests. As determined by electron paramagnetic resonance spectra, in situ Fourier transform infrared spectra, and density functional theory calculations, Nb-O-Sn bonds and built-in electric field induced by the addition of oxygen vacancies jointly accelerate the Z-scheme charge transfer for enhanced photocatalytic performance. This work provides a promising route for consciously modulating Z-scheme charge transfer by atomic-level interface engineering to boost photocatalytic performance.

Low crystalline photocatalysts with unsaturated active sites, such as oxygen vacancy (O v ), is reported to exhibit enhanced adsorption and activation of oxygen-containing small molecules, such as H 2 O and CO 2 , thus boosting the activity in photocatalytic H 2 evolution and CO 2 reduction. However, numerous low-crystalline photocatalysts show unsatisfactory stability due to the easily repaired surface O v . Herein, three SnNb 2 O 6 with different crystallinity were prepared by hydrothermal approach with similar precursors. Compared with bulk SnNb 2 O 6 and ultra-thin layered SnNb 2 O 6 , low-crystalline SnNb 2 O 6 (SNA) exhibits optimal visible-light-driven evolution rates of H 2 (86.04 μmol g -1  h -1 ) and CO from CO 2 (71.97 μmol g -1  h -1 ), which is mainly ascribed to the fast separation of the photogenerated carriers and enhanced photoreduction power caused by the surface O v . More importantly, the sharp decrease of photocatalytic activity of SNA after seven cycles is well restored by the hydrothermal treatment of recycled SNA, ascribed to the reactivated surface O v with the recovered low-crystalline structure. These works thus offer a promising strategy for developing low-crystalline and amorphous photocatalysts with high activity and stability., (© 2023 Wiley-VCH GmbH.)