WOx/C Heterogeneous Catalyst with Oxygen Vacancies and Deficient Brönsted Acid for Epoxidation of 1-Hexene

Oxygen vacancy-rich defective tungsten oxide (WO3-x) modified by Prussian blue for efficient photocatalytic carbon dioxide conversion and tetracycline degradation

The coupling of carbon dioxide (CO2) with epoxides to produce cyclic carbonates is a desirable decarbonization approach, but its commercial applicability is still restricted by the costly catalysts required, as well as the need for high temperature and high pressure. Herein, oxygen vacancy-rich defective tungsten oxide (WO3-x) rich in Lewis acid sites was modified by Prussian blue (PB), and the obtained composite reaches up to 94 % styrene carbonate yield (171 mmol g-1h-1) at ambient temperature and pressure, exhibiting outstanding advantages in the photocatalytic CO2 cycloaddition reaction compared with currently reported photocatalysts. It is found that the introduction of PB with photothermal properties significantly enhances the capability of WO3-x to absorb and activate CO2 and epoxide, along with its light utilization ability. PB acts as an electron reservoir to promote the separation and migration of photoinduced electrons and holes. The synergistic catalytic effect between photogenerated charge carriers and photothermal effect has been verified. The PB-modified WO3-x composites can also achieve complete tetracycline (TC) degradation in 30 min, and this excellent photo-Fenton TC removal activity is attributed to the combined action of active species (•OH, 1O2, h+, •O2-). This work aims to offer fresh perspectives on developing bifunctional photocatalysts for CO2 conversion and environmental remediation.

Coverage of surfactants on polyoxometalate tunes the selectivity of alkene epoxidation

Self-assembled nanosheets were formed through the interaction between polyoxometalate (POM) and dodecyltrimethylammonium bromide (DTB), which has hydrophilic groups at both ends of the carbon chain and serves as a "bridge" connecting the POMs. The resulting POM assemblies demonstrated high efficiency in the epoxidation of cyclohexene, with the selectivity of the reaction being tunable by adjusting the coverage of DTB on the POM.

Porous carbon film/WO3-x nanosheets based SERS substrate combined with deep learning technique for molecule detection

The Surface-enhanced Raman scattering (SERS) is an attractive optical detecting method with high sensitivity and detectivity, however challenges on large-area signal uniformity and complex spectra analysis methods always retards its wide application. Herein, a highly sensitive and uniform SERS detection strategy supported by porous carbon film/WO3-x nanosheets (PorC/WO3-x) based noble-metal-free SERS substrate and deep learning algorithm are reported. Experimentally, the PorC/WO3-x substrate was prepared by high-temperature annealing the PorC/WO3 films under the argon atmosphere. The defect density of the WO3 was controlled by tuning the reducing reaction time during the annealing process. The SERS performance was evaluated by using R6G as the Raman reporter, it showed that the SERS intensity obtained on the substrate with the optimal annealing time of 3 h was about 8 times as high as that obtained on the PorC/WO3 substrate without annealing treatment. And detection limit of 10-7 M and Raman enhancement factor of 106 could be achieved. Moreover, the above optimal SERS substrate was utilized to detect flavonoids of quercetin, 3-hydroxyflavone and flavone, and a deep learning algorithms was incorporated to identify the quercetin. It revealed that quercetin can be accurately detected within the above flavonoids, and lowest detectable concentration of 10-5 M can be achieved.

Insights into the effect of oxygen vacancies on the epoxidation of 1-hexene with hydrogen peroxide over WO3−x/SBA-15

The introduction of oxygen vacancies improved 1-hexene epoxidation performance over WO3−x/SBA-15 catalysts, which is attributed to the enhanced Lewis acidity of the active centers and the reduced energy barrier of the rate-determining step.