A significant synergic effect between a metal–organic framework (MOF) and Fe2SO4, the so‐called MOF+ technique, is exploited for the first time to remove toxic chromate from aqueous solutions. The results show that relative to the pristine MOF samples (no detectable chromate removal), the MOF+ method enables super performance, giving a 796 Cr mg g−1 adsorption capacity. The value is almost eight‐fold higher than the best value of established MOF adsorbents, and the highest value of all reported porous adsorbents for such use. The adsorption mechanism, unlike the anion‐exchange process that dominates chromate removal in all other MOF adsorbents, as unveiled by X‐ray photoelectron spectroscopy (XPS), scanning electron microscopy (SEM), and transmission electron microscopy (TEM), is due to the surface formation of Fe0.75Cr0.25(OH)3 nanospheres on the MOF samples.

A dual temperature- and light-responsive C H /C H separation switch in a diarylethene metal-organic framework (MOF) is presented. At 195 K and 100 kPa this MOF shows ultrahigh C H /C H selectivity of 47.1, which is almost 21.4 times larger than the corresponding value of 2.2 at 293 K and 100 kPa, or 15.7 times larger than the value of 3.0 for the material under UV at 195 K and 100 kPa. The origin of this unique control in C H /C H selectivity, as unveiled by density functional calculations, is due to a guest discriminatory gate-opening effect from the diarylethene unit.

The first MOF (metal-organic framework) built on both diarylethene and azobenzene photochromic units is reported here and displays distinct photoresponses for different guest molecules, thus creating an easy-to-use pathway to modulate the adsorption selectivity of MOF materials.

Herein, we report a robust azo-metal-organic framework (MOF), namely, ECUT-15, which can be described as a 10-connected bct net built on trinuclear Co3 subunits. The activated samples of it perform a somewhat breathing behavior. Most importantly, under UV irradiation, this MOF performs outstanding photoswitching behavior toward CO2, giving great variation in the CO2 capture/release performance, for example, 45% under static conditions and 75% under dynamic measurements, as well as instantaneous release of up to 78%.

A novel strategy for the rapid detection and identification of traditional and emerging Campylobacter strains based upon Raman spectroscopy (532 nm) is presented here. A total of 200 reference strains and clinical isolates of 11 different Campylobacter species recovered from infected animals and humans from China and North America were used to establish a global Raman spectroscopy-based dendrogram model for Campylobacter identification to the species level and cross validated for its feasibility to predict Campylobacter -associated food-borne outbreaks. Bayesian probability coupled with Monte Carlo estimation was employed to validate the established Raman classification model on the basis of the selected principal components, mainly protein secondary structures, on the Campylobacter cell membrane. This Raman spectroscopy-based typing technique correlates well with multilocus sequence typing and has an average recognition rate of 97.21%. Discriminatory power for the Raman classification model had a Simpson index of diversity of 0.968. Intra- and interlaboratory reproducibility with different instrumentation yielded differentiation index values of 4.79 to 6.03 for wave numbers between 1,800 and 650 cm −1 and demonstrated the feasibility of using this spectroscopic method at different laboratories. Our Raman spectroscopy-based partial least-squares regression model could precisely discriminate and quantify the actual concentration of a specific Campylobacter strain in a bacterial mixture (regression coefficient, >0.98; residual prediction deviation, >7.88). A standard protocol for sample preparation, spectral collection, model validation, and data analyses was established for the Raman spectroscopic technique. Raman spectroscopy may have advantages over traditional genotyping methods for bacterial epidemiology, such as detection speed and accuracy of identification to the species level.