Machine learning ensures rapid and precise selection of gold sea-urchin-like nanoparticles for desired light-to-plasmon resonance

Sustainable energy strategies, particularly solar-to-hydrogen production, are anticipated to overcome the global reliance on fossil fuels. Thereby, materials enabling the production of green hydrogen from water and sunlight are continuously designed,; e.g.; , ZnO nanostructures coated by gold sea-urchin-like nanoparticles, which employ the light-to-plasmon resonance to realize photoelectrochemical water splitting. But such light-to-plasmon resonance is strongly impacted by the size, the species, and the concentration of the metal nanoparticles coating on the ZnO nanoflower surfaces. Therefore, a precise prediction of the surface plasmon resonance is crucial to achieving an optimized nanoparticle fabrication of the desired light-to-plasmon resonance. To this end, we synthesized a substantial amount of metal (gold) nanoparticles of different sizes and species, which are further coated on ZnO nanoflowers. Subsequently, we utilized a genetic algorithm neural network (GANN) to obtain the synergistically trained model by considering the light-to-plasmon conversion efficiencies and fabrication parameters, such as multiple metal species, precursor concentrations, surfactant concentrations, linker concentrations, and coating times. In addition, we integrated into the model's training the data of nanoparticles due to their inherent complexity, which manifests the light-to-plasmon conversion efficiency far from the coupling state. Therefore, the trained model can guide us to obtain a rapid and automatic selection of fabrication parameters of the nanoparticles with the anticipated light-to-plasmon resonance, which is more efficient than an empirical selection. The capability of the method achieved in this work furthermore demonstrates a successful projection of the light-to-plasmon conversion efficiency and contributes to an efficient selection of the fabrication parameters leading to the anticipated properties

Inclusions properties at 1673 K and room temperature with Ce addition in SS400 steel

Inclusion species formed in SS400 steel with Ce-addition was predicted by thermodynamic calculation. The analysis of the inclusion morphology and size distribution was carried out by applying Scanning Electron Microscopy (SEM) and Transmission Electron Microscope (TEM). Nano-Fe3O4 particles were also found in cerium-deoxidized and -desulfurized steel and their shapes were nearly spherical. The complex Ce2O3 inclusions covering a layer of 218 nm composed by several MnS particles with similar diffraction pattern. Most importantly, the complex Ce2O3 characterized by using TEM diffraction is amorphous in the steel, indicating that Ce2O3 formed in the liquid iron and then MnS segregated cling to it

Analysis of Tourist Flow from the US to Taiwan

It is important for Taiwanese policy makers to understand how economic factors affect US tourists’ decision to travel to Taiwan. For the long-run analysis, Johansen’s cointegration test reveals that three cointegration vectors exist among the model variables, indicating a long-run relationship. To conduct a short-run analysis, this paper employs vector auto regression (VAR) to estimate the responses of US tourists in Taiwan to the shocks of changes to personal disposable income, cost of living, and substitute price. The short-run equilibrium adjustment processes are discussed in terms of generalized impulse response. The results show an immediate and significant response of changes in tourist arrivals to their own impacts, changes in the cost of living, and changes in the substitute price. In addition, the price, income, and cross-elasticity of tourism demand are all positive at the beginning of the responses, implying that the tourism products can be attributed to normal and substitute goods

5-Iodo­pyrimidin-2-amine

The mol­ecule of the title compound, C4H4IN3, has crystallographic mirror plane symmetry. In the crystal, the mol­ecules are connected through N—H⋯N hydrogen bonds into polymeric tapes extended along the a axis, which are typical of 2-amino­pyrimidines. Each mol­ecule acts as a double donor and a double acceptor in the hydrogen bonding

Analysis of Surface Texturization of Solar Cells by Molecular Dynamics Simulations

The purpose of this paper is to develop a simple new model, based on the classic molecular dynamics simulation (MD), alternative to complex electron-photon interactions to analyze the surface texturization of solar cells. This methodology can easily propose the absorptance differences between texturing and nontexturing solar cells. To verify model feasibility, this study simulates square, pyramidal, and semicircular texturization surfaces. Simulations show that surface texturization effectively increases the absorptance of incident light for solar cells, and this paper presents optimal texturization shapes. The MD model can also be potentially used to predict the efficiency promotion in any optical reflection-absorption cases