[Adsorption Characteristics of Tetracycline by CuFeO2-modified Biochar].

CuFeO2-modified biochars were prepared through co-precipitation and hydrothermal methods, and the composites had high efficiency removal for tetracycline (TC) from water. The CuFeO2-modified biochar with a 2:1 mass ratio of CuFeO2 to BC450 (CuFeO2/BC450=2:1) demonstrated the best adsorption performance. The kinetic process of TC adsorption by CuFeO2/BC450=2:1 was well fitted with the intraparticle diffusion model, suggesting that the adsorption process was controlled by film and pore diffusion. Under the condition of neutral pH and 298 K, the maximum adsorption capacity of the Langmuir model of CuFeO2/BC450=2:1 was 82.8 mg·g-1, which was much greater than that of BC450 (13.7 mg·g-1) and CuFeO2(14.8 mg·g-1). The thermodynamic data suggested that TC sorption onto CuFeO2/BC450=2:1 was a spontaneous and endothermic process. The removal of TC by CuFeO2/BC450=2:1 increased first and then decreased with increasing pH, and the maximum adsorption occurred under the neutral condition. The strong adsorption of TC by CuFeO2/BC450=2:1 could be attributed to better porosity, larger specific surface area, and more active sites (e.g., functional groups and charged surfaces). This work provided an efficient magnetic adsorbent for removing antibiotics.

Multivariate recurrence network analysis for characterizing horizontal oil-water two-phase flow.

Characterizing complex patterns arising from horizontal oil-water two-phase flows is a contemporary and challenging problem of paramount importance. We design a new multisector conductance sensor and systematically carry out horizontal oil-water two-phase flow experiments for measuring multivariate signals of different flow patterns. We then infer multivariate recurrence networks from these experimental data and investigate local cross-network properties for each constructed network. Our results demonstrate that a cross-clustering coefficient from a multivariate recurrence network is very sensitive to transitions among different flow patterns and recovers quantitative insights into the flow behavior underlying horizontal oil-water flows. These properties render multivariate recurrence networks particularly powerful for investigating a horizontal oil-water two-phase flow system and its complex interacting components from a network perspective.