RESUMEN
Improving the solar-to-thermal energy conversion efficiency of photothermal nanomaterials at no expense of other physicochemical properties, e.g., the catalytic reactivity of metal nanoparticles, is highly desired for diverse applications but remains a big challenge. Herein, a synergistic strategy is developed for enhanced photothermal conversion by a greenhouse-like plasmonic superstructure of 4 nm cobalt nanoparticles while maintaining their intrinsic catalytic reactivity. The silica shell plays a key role in retaining the plasmonic superstructures for efficient use of the full solar spectrum, and reducing the heat loss of cobalt nanoparticles via the nano-greenhouse effect. The optimized plasmonic superstructure catalyst exhibits supra-photothermal CO2 methanation performance with a record-high rate of 2.3 mol gCo -1 h-1 , close to 100% CH4 selectivity, and desirable catalytic stability. This work reveals the great potential of nanoscale greenhouse effect in enhancing photothermal conversions through the combination with conventional promoting strategies, shedding light on the design of efficient photothermal nanomaterials for demanding applications.
RESUMEN
Photothermal catalytic CO2 hydrogenation holds great promise for relieving the global environment and energy crises. The "nano-greenhouse effect" has been recognized as a crucial strategy for improving the heat management capabilities of a photothermal catalyst by ameliorating the convective and radiative heat losses. Yet it remains unclear to what degree the respective heat transfer and mass transport efficiencies depend on the specific structures. Herein, the structure-function relationship of the "nano-greenhouse effect" was investigated and optimized in a prototypical Ni@SiO2 core-shell catalyst towards photothermal CO2 catalysis. Experimental and theoretical results indicate that modulation of the thickness and porosity of the SiO2 nanoshell leads to variations in both heat preservation and mass transport properties. This work deepens the understandings on the contributing factor of the "nano-greenhouse effect" towards enhanced photothermal conversion. It also provides insights on the design principles of an ideal photothermal catalyst in balancing heat management and mass transport processes.
RESUMEN
The typical representative of pathological tremor is Parkinson's disease. One of the pathogenesis is that the synchronized neural oscillations within and between brain areas are affected. Inspired by this, this work proposes an algorithm based on neural oscillator to extract voluntary motion and estimate tremor motion in real time, which is named as RTBNO. This algorithm is composed of multiple adaptive modified Hopf oscillators linear combiner. The combiner is divided into two parts: one is used to estimate tremor motion and the other is applied to estimate voluntary motion. As it is updated iteratively in real time, this method has no phase delay. The performance of the proposed method was verified by the simulated action tremor and the actual experimental results of twenty Parkinson's disease patients. For the rest tremor signals of patients, the mean Root Mean Square Error (RMSE) values between the estimated signal and the actual signal was 0.0272±0.0077. The mean RMSE values between the estimated voluntary movement from action tremor and the actual voluntary movement were 0.0360±0.0097 (pick and put motion) and 0.0380±0.0083 (drawing motion). The execution time for the corresponding 10 seconds data was 0.0478s. The comparison results between the proposed method and the existing methods demonstrated the effectiveness of the proposed method.
