Metal-organic frameworks based on octafluorobiphenyl-4,4′-dicarboxylate: Synthesis, crystal structure, and surface functionality

In contrast to aromatic carboxylates, the coordination polymers based on their perfluorinated analogues are not numerous. Here we present a series of six Zn(ii) coordination polymers of different dimensionalities (1D, 2D, and 3D) and porosities based on octafluorobiphenyl-4,4′-dicarboxylate (oFBPDC2-) and N-containing co-ligands (ur, dabco, and bpy). These complexes are characterized by single-crystal X-ray diffraction, PXRD, FT-IR, elemental analysis, and TGA. The metal-organic frameworks [Zn2(CH3CONH2)2(oFBPDC)2] (1) and [Zn2(oFBPDC)2(dabco)] (4) are shown to be porous with BET surface areas of 470 m2 g-1 and 441 m2 g-1, respectively. In addition, compound 4 shows selectivity factors of 11.3, 4.9 and more than 6 for the binary gas mixtures CO2/N2, CO2/CH4 and benzene/cyclohexane, respectively. The measurements for pressed powders and water droplet give water contact angles of 136° for 4 and 133° for (H2bpy)[Zn2(bpy)(oFBPDC)3] (5). Low water uptake indicates that both 4 and 5 belong to highly hydrophobic solids. © The Royal Society of Chemistry

A stochastic model for ecological systems with strong nonlinear response to environmental drivers: application to two water-borne diseases

Ecological systems with threshold behaviour show drastic shifts in population abundance or species diversity in response to small variation in critical parameters. Examples of threshold behaviour arise in resource competition theory, epidemiological theory and environmentally driven population dynamics, to name a few. Although expected from theory, thresholds may be difficult to detect in real datasets due to stochasticity, finite population size and confounding effects that soften the observed shifts and introduce variability in the data. Here, we propose a modelling framework for threshold responses to environmental drivers that allows for a flexible treatment of the transition between regimes, including variation in the sharpness of the transition and the variance of the response. The model assumes two underlying stochastic processes whose mixture determines the system's response. For environmentally driven systems, the mixture is a function of an environmental covariate and the response may exhibit strong nonlinearity. When applied to two datasets for water-borne diseases, the model was able to capture the effect of rainfall on the mean number of cases as well as the variance. A quantitative description of this kind of threshold behaviour is of more general application to predict the response of ecosystems and human health to climate change