Stimulus-responsive materials have great potential in advanced controllable oil/water separation applications. Here, a novel, cost-effective, and green approach is developed to produce a pH-responsive smart fabric with switchable wettability. The approach first involves grafting polydopamine (PDA) and cystamine dihydrochloride (cystamine) on a fabric surface to obtain thiol-functionalized fabric (Fabric-SH). Hydrophobic stearyl methacrylate (SMA) and pH-responsive undecylenic acid are then decorated on the Fabric-SH surface through efficient and green photoinduced thiol–ene click coupling chemistry. The obtained fabric exhibits rapidly switchable wettability between superhydrophobicity and superhydrophilicity depending on the contacting liquid pH value and can be applied in controllable separation of various mixtures of water and oil with high efficiency up to 99%. More importantly, the as-prepared fabric is able to realize the separation of oil/water/oil ternary mixtures and can self-clean and repel oil fouling during the separation process. Its superhydrophobicity is robust, showing no significant change after a 500 cycle peeling test. This novel and cost-effective smart cotton fabric exhibits significant potential in satisfying different separation purposes under complicated conditions.

Surface air temperatures (SATs) derived from the European Centre for Medium‐Range Weather Forecasts (ECMWF) ERA‐Interim and CERA‐20C reanalysis data sets are compared with data from 43 observation stations in Sichuan for 1979–2010. The results show (a) the temperatures from the ERA‐Interim and CERA‐20C data sets are strongly correlated with those from the observation stations, although significant cold biases are seen on both annual and seasonal timescales. (b) The biases in SATs are predominately influenced by the differences between the actual topography and the topography used in the reanalysis models. Larger differences in temperature are observed in the plateau and mountainous regions of Sichuan. We confirmed larger SAT biases at high altitudes by categorizing the elevation into four bands, each with a spacing of 1,000 m. (c) We reduced the biases resulting from elevation by using an elevation correction method with internal lapse rates derived from different reanalysis pressure levels. The annual mean bias was reduced from −2.86 to −0.75°C for the ERA‐Interim data set and from −5.27 to −2.21°C for the CERA‐20C data set. After calibration, the correlation coefficients between the difference in SAT (observed minus reanalysis data) and the difference in elevation (station elevation minus model elevation) decreased from −0.97 and −0.91 to −0.29 and −0.30 for the ERA‐Interim and CERA‐20C data sets, respectively. These significant differences should not be ignored in the application of reanalysis data sets to climate research. The evaluation and calibration of reanalysis data sets are essential before making assessments of regional climate change, especially over regions with complex topography.

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