The Korea Superconducting Tokamak Advanced Research (KSTAR) project is the major effort of the national fusion programme of the Republic of Korea. Its aim is to develop a steady state capable advanced superconducting tokamak to establish a scientific and technological basis for an attractive fusion reactor. The major parameters of the tokamak are: major radius 1.8 m, minor radius 0.5 m, toroidal field 3.5 T and plasma current 2 MA, with a strongly shaped plasma cross-section and double null divertor. The initial pulse length provided by the poloidal magnet system is 20 s, but the pulse length can be increased to 300 s through non-inductive current drive. The plasma heating and current drive system consists of neutral beams, ion cyclotron waves, lower hybrid waves and electron cyclotron waves for flexible profile control in advanced tokamak operating modes. A comprehensive set of diagnostics is planned for plasma control, performance evaluation and physics understanding. The project has completed its conceptual design and moved to the engineering design and construction phase. The target date for the first plasma is 2002.

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Noble metal nanoparticle decoration is a representative strategy to enhance selectivity for fabricating chemical sensor arrays based on the 2-dimensional (2D) semiconductor material, represented by molybdenum disulfide (MoS2). However, the mechanism of selectivity tuning by noble metal decoration on 2D materials has not been fully elucidated. Here, we successfully decorated noble metal nanoparticles on MoS2 flakes by the solution process without using reducing agents. The MoS2 flakes showed drastic selectivity changes after surface decoration and distinguished ammonia, hydrogen, and ethanol gases clearly, which were not observed in general 3D metal oxide nanostructures. The role of noble metal nanoparticle decoration on the selectivity change is investigated by first-principles density functional theory (DFT) calculations. While the H2 sensitivity shows a similar tendency with the calculated binding energy, that of NH3 is strongly related to the binding site deactivation due to preferred noble metal particle decoration at the MoS2 edge. This finding is a specific phenomenon which originates from the distinguished structure of the 2D material, with highly active edge sites. We believe that our study will provide the fundamental comprehension for the strategy to devise the highly efficient sensor array based on 2D materials.

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For the automation of a laser beam welding (LBW) process, the weld quality must be monitored without destructive testing, and the quality must be assessed. A deep neural network (DNN)-based quality assessment method in spectrometry-based LBW is presented in this study. A spectrometer with a response range of 225–975 nm is designed and fabricated to measure and analyze the light reflected from the welding area in the LBW process. The weld quality is classified through welding experiments, and the spectral data are thus analyzed using the spectrometer, according to the welding conditions and weld quality classes. The measured data are converted to RGB (red, green, blue) values to obtain standardized and simplified spectral data. The weld quality prediction model is designed based on DNN, and the DNN model is trained using the experimental data. It is seen that the developed model has a weld-quality prediction accuracy of approximately 90%.