RESUMEN
Efficient metal-organic frameworks (MOFs) photocatalytic bactericidal catalysts are urgently needed in water purification. Herein, a Fe-MOF (MIL-88B-NH2(V1Fe5) with promoted electron transport was achieved by vanadium (V) ions doping and V/Fe ratio optimization, showing excellent photocatalytic bactericidal activity againstE. coliunder visible light irradiation (99.92%). The efficient antibacterial mechanism, V as a Ti-like mediator boosting electronic transmission in MIL-88B-NH2(V1Fe5), was revealed by its band structure, transient photocurrent, electrochemical impedance spectroscopy, and scavenger quenching experiments. The enhancement of photocatalytic bactericidal performance of Fe-MOFs by V-ion-doping was confirmed by two other Fe-MOFs, MIL-53-NH2(V1Fe5) and MIL-101-NH2(V1Fe5), with the same metal ions and ligands, both of which have higher performance than the corresponding undoped MOFs. Among them, MIL-88B-NH2(V1Fe5) exhibits the highest photocatalytic bactericidal activity due to its suitable metal clusters ([M(µ3-O)] cluster) and topological structure (three-dimensional rhomboid network structure). This work demonstrated the amplification effect of V ion doping on electron transport in Fe-MOFs photocatalysts.
RESUMEN
Adaptive gradient algorithms have been successfully used in deep learning. Previous work reveals that adaptive gradient algorithms mainly borrow the moving average idea of heavy ball acceleration to estimate the first- and second-order moments of the gradient for accelerating convergence. However, Nesterov acceleration which uses the gradient at extrapolation point can achieve a faster convergence speed than heavy ball acceleration in theory. In this article, a new optimization algorithm which combines adaptive gradient algorithm with Nesterov acceleration by using a look-ahead scheme, called NALA, is proposed for deep learning. NALA iteratively updates two sets of weights, i.e., the 'fast weights' in its inner loop and the 'slow weights' in its outer loop. Concretely, NALA first updates the fast weights k times using Adam optimizer in the inner loop, and then updates the slow weights once in the direction of Nesterov's Accelerated Gradient (NAG) in the outer loop. We compare NALA with several popular optimization algorithms on a range of image classification tasks on public datasets. The experimental results show that NALA can achieve faster convergence and higher accuracy than other popular optimization algorithms.
RESUMEN
A series of low-dose high-valence Ti4+ doped MIL-53-NH2(Fe) photocatalysts were synthesized for visible-light-driven CO2 reduction. The highest CO2-to-CO conversion rate of Ti4+ doped MIL-53-NH2(Fe) was 7.24 mmol g-1 h-1 and the highest CO selectivity was 94% in acetonitrile solvent using [Ru(bpy)3]2+ and triethanolamine.