The synthesis of dimethyl carbonate (DMC) from CO 2 and methanol by Zr-doped CeO 2 nanorods with different ratios of Zr/Ce has been studied at 6.8 MPa and 140 °C. The catalysts were characterized extensively by TEM, XRD, N 2 adsorption, Raman spectroscopy, UV−vis spectroscopy, XPS, CO 2 -TPD, and in situ FTIR techniques. Doping of Zr atoms into the ceria lattice produced a fluorite-like solid solution, promoting the formation of oxygen vacancy sites. Zr-doped CeO 2 nanorods exhibited significantly more oxygen vacancy sites than pure CeO 2 nanorods. Zr 0.1 Ce nanorods which exhibited DMC synthesis activity also possess the highest concentration of oxygen vacancy sites. In situ FTIR studies further revealed that CO 2 can adsorb on the oxygen vacancy to form bidentate carbonate and as intermediate to participate in the reaction. This study presents a strategy to design a high-efficiency CeO 2 -based catalysts by controlling the concentration of the surface oxygen vacancies.

The accurate distribution of countercations (Rb+ and Sr2+) around a rigid, spherical, 2.9‐nm size polyoxometalate cluster, {Mo132}42−, is determined by anomalous small‐angle X‐ray scattering. Both Rb+ and Sr2+ ions lead to shorter diffuse lengths for {Mo132} than prediction. Most Rb+ ions are closely associated with {Mo132} by staying near the skeleton of {Mo132} or in the Stern layer, whereas more Sr2+ ions loosely associate with {Mo132} in the diffuse layer. The stronger affinity of Rb+ ions towards {Mo132} than that of Sr2+ ions explains the anomalous lower critical coagulation concentration of {Mo132} with Rb+ compared to Sr2+. The anomalous behavior of {Mo132} can be attributed to majority of negative charges being located at the inner surface of its cavity. The longer anion–cation distance weakens the Coulomb interaction, making the enthalpy change owing to the breakage of hydration layers of cations more important in regulating the counterion–{Mo132} interaction.

No abstract

The accurate distribution of countercations (Rb+ and Sr2+) around a rigid, spherical, 2.9‐nm size polyoxometalate cluster, {Mo132}42−, is determined by anomalous small‐angle X‐ray scattering. Both Rb+ and Sr2+ ions lead to shorter diffuse lengths for {Mo132} than prediction. Most Rb+ ions are closely associated with {Mo132} by staying near the skeleton of {Mo132} or in the Stern layer, whereas more Sr2+ ions loosely associate with {Mo132} in the diffuse layer. The stronger affinity of Rb+ ions towards {Mo132} than that of Sr2+ ions explains the anomalous lower critical coagulation concentration of {Mo132} with Rb+ compared to Sr2+. The anomalous behavior of {Mo132} can be attributed to majority of negative charges being located at the inner surface of its cavity. The longer anion–cation distance weakens the Coulomb interaction, making the enthalpy change owing to the breakage of hydration layers of cations more important in regulating the counterion–{Mo132} interaction.

Plant-based meat analogs are food products that mimic the appearance, texture, and taste of real meat. The development process requires laborious experimental iterations and expert knowledge to meet consumer expectations. To address these problems, we propose a machine learning (ML)-based framework to predict the textural properties of meat analogs. We introduce the proximate compositions of the raw materials, namely protein, fat, carbohydrate, fibre, ash, and moisture, in percentages and the “targeted moisture contents” of the meat analogs as input features of the ML models, such as Ridge, XGBoost, and MLP, adopting a build-in feature selection mechanism for predicting “Hardness” and “Chewiness”. We achieved a mean absolute percentage error (MAPE) of 22.9%, root mean square error (RMSE) of 10.101 for Hardness, MAPE of 14.5%, and RMSE of 6.035 for Chewiness. In addition, carbohydrates, fat and targeted moisture content are found to be the most important factors in determining textural properties. We also investigate multicollinearity among the features, linearity of the designed model, and inconsistent food compositions for validation of the experimental design. Our results have shown that ML is an effective aid in formulating plant-based meat analogs, laying out the groundwork to expediently optimize product development cycles to reduce costs.