Photochemical degradation of typical phthalate esters, as, e.g. dimethyl phthalate (DMP), in water by the UV/H2O2 process was investigated. The degradation rate of DMP was affected by several reaction factors, including initial DMP concentration, H2O2 concentration, UV light intensity, and coexisting cations and anions. The DMP degradation rate decreased with increasing initial DMP concentration. Carbonate ions significantly reduced the reaction rate by 70.2%, while the degradation rate was improved ten‐fold in the presence of ferric ions. The initial reaction mechanism and possible degradation products were investigated using density functional theory (DFT), including B3LYP and M062X hybrid functional. The addition of OH radicals (OH•) to the C4 position of the DMP ring is energetically favored for the initial reaction of DMP with OH•. Dimethyl 4‐hydroxyphthalate (DMP‐4OH) is one of the initial degradation intermediates. A possible reaction mechanism was also proposed based on GC/MS analysis and quantum chemical calculations. The formation of DMP‐4OH has been explained by quantum chemical calculations, which indicates the advantage and promising role of this method in the degradation mechanism analysis.

Benzophenone-3 (BP-3), as an important organic UV filter, is widely used in the sunscreen, cosmetic, and personal care products. The chemical reaction mechanism and kinetics of BP-3 degradation initiated by hydroxyl (OH) radical was investigated in the atmosphere based on the density functional theory (DFT). The results showed that the OH radical is more easily added to the C3 position of the aromatic ring (pathway 3), while the H atom abstraction from the OH group on the aromatic ring (pathway 23) is an energetically favorable reaction pathway. At ambient temperature, 298 K, the overall rate constant for the primary reaction is about 1.50 × 10

Facial expression recognition plays a key role in human-computer emotional interaction. However, human faces in real environments are affected by various unfavorable factors, which will result in the reduction of expression recognition accuracy. In this paper, we proposed a novel method which combines Fine-tuning Swin Transformer and Multiple Weights Optimality-seeking (FST-MWOS) to enhanced expression recognition performance. FST-MWOS mainly consists of two crucial components: Fine-tuning Swin Transformer (FST) and Multiple Weights Optimality-seeking (MWOS). FST takes Swin Transformer Large as the backbone network to obtain multiple groups of fine-tuned model weights for the homologous data domains by hyperparameters configurations, data augmentation methods, etc. In MWOS a greedy strategy was used to mine locally optimal generalizations in the optimal epoch interval of each group of fine-tuned model weights. Then, the optimality-seeking for multiple groups of locally optimal weights was utilized to obtain the global optimal solution. Experiments results on RAF-DB, FERPlus and AffectNet datasets show that the proposed FST-MWOS method outperforms various state-of-the-art methods.

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