Metal-organic frameworks(MOFs),renowned for structural diversity and design flexibility,exhibit potential in catalysis.However,the pursuit of higher catalytic activity through defects often compromises stability,requi...Metal-organic frameworks(MOFs),renowned for structural diversity and design flexibility,exhibit potential in catalysis.However,the pursuit of higher catalytic activity through defects often compromises stability,requiring a delicate balance.Traditional trial-and-error method for optimizing synthesis parameters within the complex chemical space is inefficient.Herein,taking the typical MOF UiO-66(Ce)as an illustrative example,a closed loop workflow is built,which integrates ma-chine learning(ML)-assissted prediction,multi-objective optimization(MOO)and experimental preparation to synergistically optimize the defect content and thermal stability of UiO-66(Ce)for efficient hydrogenation of dicyclopentadiene(DCPD).An automatic data extraction program ensures data accuracy,establishing a high-quality database.ML is employed to explore the intricate synthesis-structure-property correlations,enabling precise delineation of pure-phase subspace and accurate predictions of properties.After two iterations,MOO model identifies optimal protocols for high defect content(>40%)and thermal stability(>300℃).The optimized UiO-66(Ce)exhibits superior catalytic performance in hydroge-nation of DCPD,validating the precision and reliability of our methodology.This ML-assisted approach offers a valuable paradigm for solving the trade-off riddle in materials field.展开更多
Effect of retanning on the thermal stability of leather is eliciting increasing attention. However, the relationship between the hydrophilicity of retanning agents and the heat resistance of leather and the correspond...Effect of retanning on the thermal stability of leather is eliciting increasing attention. However, the relationship between the hydrophilicity of retanning agents and the heat resistance of leather and the corresponding mechanism remain unclear. Herein, phenolic formaldehyde syntans (PFSs) were selected as models to explore the effect of the hydrophilicity of retanning agents on the thermal stability of retanned leather. The thermal stability of leather was closely correlated to the hydrophilic group content (sulfonation degree) of PFSs. As the sulfonation degree increased, the water absorption rate of PFSs and their retanned leathers decreased, whereas the thermal stability of leather increased. Molecular dynamics simulation results proved that the introduction of PFSs could reduce the binding ability of collagen molecules with water and thus decreased the water molecules around the PFS-treated collagen. These results may provide guidance for the tanners to select retanning agents reasonably to improve the thermal stability of leather.展开更多
基金supported by the National Key R&D Program of China(Grant No.2021YFB3500700)Beijing Natural Science Foundation(Grant No.L233011)Guangdong Basic and Applied Basic Research Foundation(Grant No.2022A1515010185).
文摘Metal-organic frameworks(MOFs),renowned for structural diversity and design flexibility,exhibit potential in catalysis.However,the pursuit of higher catalytic activity through defects often compromises stability,requiring a delicate balance.Traditional trial-and-error method for optimizing synthesis parameters within the complex chemical space is inefficient.Herein,taking the typical MOF UiO-66(Ce)as an illustrative example,a closed loop workflow is built,which integrates ma-chine learning(ML)-assissted prediction,multi-objective optimization(MOO)and experimental preparation to synergistically optimize the defect content and thermal stability of UiO-66(Ce)for efficient hydrogenation of dicyclopentadiene(DCPD).An automatic data extraction program ensures data accuracy,establishing a high-quality database.ML is employed to explore the intricate synthesis-structure-property correlations,enabling precise delineation of pure-phase subspace and accurate predictions of properties.After two iterations,MOO model identifies optimal protocols for high defect content(>40%)and thermal stability(>300℃).The optimized UiO-66(Ce)exhibits superior catalytic performance in hydroge-nation of DCPD,validating the precision and reliability of our methodology.This ML-assisted approach offers a valuable paradigm for solving the trade-off riddle in materials field.
基金the National Natural Science Foundation of China(21978176).
文摘Effect of retanning on the thermal stability of leather is eliciting increasing attention. However, the relationship between the hydrophilicity of retanning agents and the heat resistance of leather and the corresponding mechanism remain unclear. Herein, phenolic formaldehyde syntans (PFSs) were selected as models to explore the effect of the hydrophilicity of retanning agents on the thermal stability of retanned leather. The thermal stability of leather was closely correlated to the hydrophilic group content (sulfonation degree) of PFSs. As the sulfonation degree increased, the water absorption rate of PFSs and their retanned leathers decreased, whereas the thermal stability of leather increased. Molecular dynamics simulation results proved that the introduction of PFSs could reduce the binding ability of collagen molecules with water and thus decreased the water molecules around the PFS-treated collagen. These results may provide guidance for the tanners to select retanning agents reasonably to improve the thermal stability of leather.
