Particulate sulfate is one of the most important components in the atmosphere. The observation of rapid sulfate aerosol production during haze events provoked scientific interest in the multiphase oxidation of SO 2 in aqueous aerosol particles. Diverse oxidation pathways can be enhanced or suppressed under different aerosol acidity levels and high ionic strength conditions of atmospheric aerosol. The importance of ionic strength to sulfate multiphase chemistry has been verified under laboratory conditions, though studies in the actual atmosphere are still limited. By utilizing online observations and developing an improved solute strengthdependent chemical thermodynamics and kinetics model (EF-T&K model, EF is the enhancement factor that represents the effect of ionic strength on an aerosol aqueous-phase reaction), we provided quantitative evidence that the H 2 O 2 pathway was enhanced nearly 100 times and dominated sulfate formation for entire years (66%) in Tianjin (a northern city in China). TMI (oxygen catalyzed by transition-metal ions) (14%) and NO 2 (14%) pathways got the second-highest contributions. Machine learning supported the result that aerosol sulfate production was more affected by the H 2 O 2 pathway. The collaborative effects of atmospheric oxidants and SO 2 on sulfate aerosol production were further investigated using the improved EF-T&K model. Our findings highlight the effectiveness of adopting target oxidant control as a new direction for sustainable mitigation of sulfate, given the already low SO 2 concentrations in China.

Surfactants exhibit characteristic phenomena, including the reduction of interfacial free energy, self-assembly into aggregates, and even the formation of lyotropic liquid crystalline phases at high concentrations. Our research has shown that a semifluorinated phosphonic acid can act as the two-dimensional analogue of a surfactant-a linactant-by reducing the line tension between hydrocarbon-rich and fluorocarbon-rich phases in a Langmuir monolayer. This linactant can also self-assemble into nanoscale clusters in a monolayer. Here, we explore the dependence of linactant behavior on molecular structure. Members of a homologous series of partially fluorinated phosphonic acids were synthesized and tested as linactants: CF(3)(CF(2))(n-1)(CH(2))(m)PO(3)H (abbreviated as FnHmPO(3)). The tests revealed that linactants with longer hydrophobic chains were most efficient in lowering line tension. For linactants with the same overall chain length, the length of the fluorocarbon block was correlated with efficiency. Thus, the linactant efficiency was ranked in the order F8H8PO(3) < F10H6PO(3) < F8H11PO(3) < F10H9PO(3). In all cases, linactant-containing Langmuir-Blodgett monolayers exhibited nanoscale molecular clusters with characteristic dimensions of 20-30 nm; enhanced linactant efficiency was systematically correlated with larger clusters.

No abstract

No abstract

No abstract

No abstract

No abstract

Using statistical textons for texture classification has shown great success recently. The maximal response 8 (Statistical_MR8), image patch (Statistical_Joint) and locally invariant fractal (Statistical_Fractal) are typical statistical texton algorithms and state-of-the-art texture classification methods. However, there are two limitations when using these methods. First, it needs a training stage to build a texton library, thus the recognition accuracy will be highly depended on the training samples; second, during feature extraction, local feature is assigned to a texton by searching for the nearest texton in the whole library, which is time consuming when the library size is big and the dimension of feature is high. To address the above two issues, in this paper, three binary texton counterpart methods were proposed, Binary_MR8, Binary_Joint, and Binary_Fractal. These methods do not require any training step but encode local feature into binary representation directly. The experimental results on the CUReT, UIUC and KTH-TIPS databases show that binary texton could get sound results with fast feature extraction, especially when the image size is not big and the quality of image is not poor.