Zinc ferrite is a type of photocatalytic material with high physicochemical stability, narrow band gap, high carrier separation efficiency, high porosity, and paramagnetism, which makes it easy to recover. Thus, zinc ferrite is widely used as a photocatalyst in water treatment. In this paper, the preparation principles as well as the advantages and disadvantages of typical methods used to prepare zinc ferrite including hydrothermal, co-precipitation, sol-gel, and other novel methods such as biosynthesis have been summarized. Modification methods such as elemental doping, composite formation, and morphological modification have been highlighted. Using these modification methods, the catalytic activity of zinc ferrite toward the photocatalytic degradation of organic pollutants in water has been enhanced. Biosynthesis is regarded as a promising preparation method that uses biological materials instead of chemical materials to achieve the large-scale preparation of zinc ferrite using low cost, energy efficient, and environmentally friendly processes. Meanwhile, the combination of multiple modification techniques to enhance the photocatalytic performance of zinc ferrite will be an important research trend in the future.

Due to its highly unreactive nature and advanced biocompatibility, niobium (Nb) coating films are increasingly being used to improve the corrosion resistance and biocompatibility of base implant materials. However, Nb films have relatively low yield strengths and surface hardness; therefore, it is necessary to explore a simple and low-cost method to improve their mechanical properties. Magnetron sputtering is a commonly used tool for Nb film deposition. Applying substrate bias can introduce Ar⁺ bombard to the film surface, which is effective to improve the film’s mechanical properties. As the direct current (DC) bias-sputtering tool requires an extra DC power supply, applying the negative bias by a radio frequency (RF) power source (usually installed in the sputtering system to conduct substrate pre-cleaning) will be more economical and convenient. Moreover, the RF bias was accompanied with higher ion density and energy compared to the DC bias. In this study, Nb films were deposited on silicon wafers by magnetron sputtering under different RF bias powers. The effects of the RF bias on the structural parameters and mechanical properties of the films were studied via stress measurements, X-ray diffraction, and indentation tests. The results show that the RF bias can change the crystal distribution, grain size, and lattice parameter of the film, as well as the mechanical properties. The stress of the Nb film was compressive; it increased markedly when an RF power was applied and saturated when the RF power was over 40 W. The hardness of the film increased from 4.17 GPa to 5.34 GPa with an elevating RF power from 0 W to 60 W. This study aimed to enhance the mechanical properties of the Nb films deposited by RF-biased sputtering, which provides wider potentials for Nb film as protective coatings for medical–biological implant bodies. Although the research was carried out on Si substrates to facilitate the study of film stress, we believe that the evolution trends of our results will also apply to other metal substrates, because the measured film mechanical properties are intrinsic.

As an important reference for assessing plant water consumption and estimating plant transpiration, it is of great significance to achieve accurate prediction of plant sap flow. A number of deep learning models were established and compared using approximately 3 years of continuous eucalyptus flow time series data collected from the SAPFLUXNET open dataset and 6 environmental factors, including shortwave solar incident radiation, air temperature, air relative humidity, net radiation, vapor pressure deficit, and photosynthetic photon flux density. The experimental results show that the improved Transformer model, with the introduction of a two-step self-attention mechanism and simplified design, maintains significant predictive performance advantages compared to the original Transformer model, long short-term memory, gated recurrent unit, and temporal convolutional neural network models. In the shorter 1-h forecast, the mean squared error and coefficient of determination ( R ² ) of the improved Transformer model are 0.0191 and 0.965, respectively. Compared to the suboptimal typical Transformer model, the MSE is reduced by 22.9%, and R ² is increased by 1.0%. Additionally, the improved model maintains stable predictive performance advantages in long-term plant flow prediction. In the longest 8-h advance prediction, the MSE is reduced by 14.9% compared to the suboptimal Transformer model, and R ² increases by 3.0% compared to the Transformer model. The comprehensive experimental results show that the improved Transformer model makes more effective use of environmental information to achieve more accurate and long-term plant flow prediction. This study emphasizes the basic principle and validity of the two-step self-attention network structure and provides a valuable basis for developing more effective methods for predicting plant sap flow.