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Graphdiyne (GDY) is considered a very attractive support for metal nanocatalysts due to its unique structure and superior properties. The metal–GDY interaction can significantly affect the performance of catalysts. Herein, GDY nanotubes abundant in in situ formed Cu quantum dots (QDs) (Cu-GDYNT) are prepared using the electrospun polyacrylonitrile nanofibers collected on the surface of electrolytic Cu foil as templates. The diameter of the Cu-GDYNT is controllable and the uniform size of the embedded Cu QDs is about 2.2 nm. And then, the uniformly dispersed and highly active supported catalysts of ruthenium nanoparticles (Rux/Cu-GDYNT) are produced using the Cu-GDYNT as the support. Among them, the Ru3/Cu-GDYNT exhibit outstanding HER performance at all pH levels. Only 17, 67 and 83 mV overpotential is required to reach a current density of 10 mA cm−2 in 1.0 M KOH, 0.5 M H2SO4 and 1.0 M neutral PBS solutions, respectively. The sample exhibits 3000 CV cycle stability and 20 h continuous electrolysis without performance degradation in an alkaline medium. This work provides a new idea for constructing the GDY-supported metal nanocatalysts.

Three-dimensional digital technology has made breakthroughs and shown unique advantages in all walks of life. On the basis of practicality, the three-dimensional artistic design of ceramic products gradually adds some aesthetic, artistic design elements, which brings beautiful enjoyment to people's lives and makes people's lives colorful. This paper presents a three-dimensional artistic design method for ceramic products based on RNN (recurrent neural network) technology. With the establishment of the 3D YOLOv3 framework, the new model training is faster and more stable, the convergence speed of the loss function is faster, and the reconstructed 3D model is more accurate. After training for a certain number of times, the network gradually becomes stable, the accuracy rate is kept at 95%, and the loss function value is reduced below 0.2. The accuracy of the network model and the precision of semantic segmentation are improved. The semantic segmentation and object recognition under 3D scene reconstruction studied in this paper have certain theoretical value and high feasibility. [ABSTRACT FROM AUTHOR]

Graphdiyne (GDY) is considered a very attractive support for metal nanocatalysts due to its unique structure and superior properties. The metal–GDY interaction can significantly affect the performance of catalysts. Herein, GDY nanotubes abundant in in situ formed Cu quantum dots (QDs) (Cu-GDYNT) are prepared using the electrospun polyacrylonitrile nanofibers collected on the surface of electrolytic Cu foil as templates. The diameter of the Cu-GDYNT is controllable and the uniform size of the embedded Cu QDs is about 2.2 nm. And then, the uniformly dispersed and highly active supported catalysts of ruthenium nanoparticles (Rux/Cu-GDYNT) are produced using the Cu-GDYNT as the support. Among them, the Ru3/Cu-GDYNT exhibit outstanding HER performance at all pH levels. Only 17, 67 and 83 mV overpotential is required to reach a current density of 10 mA cm−2 in 1.0 M KOH, 0.5 M H2SO4 and 1.0 M neutral PBS solutions, respectively. The sample exhibits 3000 CV cycle stability and 20 h continuous electrolysis without performance degradation in an alkaline medium. This work provides a new idea for constructing the GDY-supported metal nanocatalysts. [ABSTRACT FROM AUTHOR]