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The interaction of CO with structurally well-defined, planar Au/TiO 2 model catalysts at elevated pressures (up to 50 mbar) was studied in-situ by polarization-modulated infrared reflection absorption spectroscopy and ex-situ by X-ray photoelectron spectroscopy performed before and after CO exposure. The results indicate a CO-induced partial reduction of the oxide surface, which is evidenced by a low frequency C-O vibration at 2060 cm )1 , combined with a spreading of the Au nanoparticles due to a modification of the Au-oxide interface energy. In a 2:1 CO:O 2 atmosphere, TiO 2 support reduction was not observed, and a pre-reduced surface was re-oxidized. The consequences of these results for the understanding of the CO oxidation mechanism on Au/TiO 2 (model) catalysts are discussed.

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The Li-S battery is a promising next-generation technology due to its high theoretical energy density (2600 Wh kg −1) and low active material cost. However, poor cycling stability and coulombic efficiency caused by polysulfide dissolution have proven to be major obstacles for a practical Li-S battery implementation. In this work, we develop a novel strategy to suppress polysulfide dissolution using hydrofluoroethers (HFEs) with bi-functional, amphiphlic surfactant-like design: a polar lithiophilic "head" attached to a fluorinated lithiophobic "tail." A unique solvation mechanism is proposed for these solvents whereby dissociated lithium ions are readily coordinated with lithiophilic "head" to induce self-assembly into micelle-like complex structures. Complex formation is verified experimentally by changing the additive structure and concentration using small angle X-ray scattering (SAXS). These HFE-based electrolytes are found to prevent polysulfide dissolution and to have excellent chemical compatibility with lithium metal: Li||Cu stripping/plating tests reveal high coulombic efficiency (>99.5%), modest polarization, and smooth surface morphology of the uniformly deposited lithium. Li-S cells are demonstrated with 1395 mAh g −1 initial capacity and 71.9% retention over 100 cycles at >99.5% efficiency-evidence that the micelle structure of the amphiphilic additives in HFEs can prohibit polysulfide dissolution while enabling facile Li + transport and anode passivation.

Objectives To investigate the pathogenicity of Klebsiella pneumoniae (KPN) possessing rmpA or the aerobactin gene in infected mice. Methods BALB/c mice were divided into four groups (n = 10 per group) and infected with: string test-positive and rmpA-positive or aerobactin-positive KPN (group 1), string test-negative but rmpA-positive KPN (group 2), string test-negative but aerobactin-positive KPN (group 3), or string test- and rmpA/aerobactin-negative KPN (group 4). Mouse survival time was compared among groups, and the infection of livers, spleens, lungs, and kidneys and KPN growth were assessed in infected mice. Results Compared with the negative group (group 4), the survival rates of mice infected with rmpA- or aerobactin-positive KPN (groups 1–3) were significantly lower, their multi-organ injuries were significantly more severe, and bacterial enumeration was significantly higher. Conclusions Despite being string test-negative, aerobactin- or rmpA-positive KPN still exhibit high virulence and anti-immune effect activity. Therefore, the combination of the string test and gene detection of aerobactin and rmpA will be helpful in screening hypervirulent KPN.

The concept of spatial interpolation is important in the soil sciences. However, the use of a single global interpolation model is often limited by certain conditions (e.g., terrain complexity), which leads to distorted interpolation results. Here we present a method of adaptive weighting combined environmental variables for soil properties interpolation (AW-SP) to improve accuracy. Using various environmental variables, AW-SP was used to interpolate soil potassium content in Qinghai Lake Basin. To evaluate AW-SP performance, we compared it with that of inverse distance weighting (IDW), ordinary kriging, and OK combined with different environmental variables. The experimental results showed that the methods combined with environmental variables did not always improve prediction accuracy even if there was a strong correlation between the soil properties and environmental variables. However, compared with IDW, OK, and OK combined with different environmental variables, AW-SP is more stable and has lower mean absolute and root mean square errors. Furthermore, the AW-SP maps provided improved details of soil potassium content and provided clearer boundaries to its spatial distribution. In conclusion, AW-SP can not only reduce prediction errors, it also accounts for the distribution and contributions of environmental variables, making the spatial interpolation of soil potassium content more reasonable.

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