Developing a novel ammonia synthesis process from N2 and H2 is of interest to the catalysis and hydrogen research communities. γ-Alumina-supported nickel was determined capable of serving as an efficient catalyst for ammonia synthesis using nonthermal plasma under atmospheric pressure without heating. The catalytic activity was almost unrelated to the crystal structure and the surface area of the alumina carrier. The activity of Ni/Al2O3 was quantitatively compared with that of Fe/Al2O3 and Ru/Al2O3, which contained active metals for the conventional Haber–Bosch process. The activity sequence was Ni/Al2O3 > Al2O3 > Fe/Al2O3 > no additive > Ru/Al2O3, surprisingly indicating that the loading of Fe and Ru decreased the activity of Al2O3. The catalytic activity of Ni/Al2O3 was dependent on the amount of loaded Ni, the calcination temperature, and the reaction time. XRD, visual, and XPS observations of the catalysts before the plasma reaction indicated the generation of NiO and NiAl2O4 on Al2O3, the latter of which was generated upon high-temperature calcination. The NiO species was readily reduced to Ni metal in the plasma reaction, whereas the NiAl2O4 species was difficult to reduce. The catalytic behavior could be attributed to the production of fine Ni metal particles that served as active sites. The PN2/PH2 ratio dependence and rate constants of formation and decomposition of ammonia were finally determined for 5.0 wt% Ni/Al2O3 calcined at 773 K. The ammonia yield was 6.3% at an applied voltage of 6.0 kV, a residence time of reactant gases of 0.12 min, and PH2/PN2 = 1.

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Local vibration testing can be used to identify cracks within reinforced concrete (RC) structures; however, achieving high accuracy in the damage evaluation is a challenge. The amount of wave propagation across a crack is affected by several factors such as aggregate contact at the crack face. This study proposes a crack model that simplifies those factors in the wave propagation analysis based on the finitedifference time-domain (FDTD) method. The simplification is achieved by blocking the wave propagation across macro-crack that have a width larger than approximately 0.1 mm. The proposed crack model is validated by comparing numerical analysis and experimental results. Furthermore, a machine learning classifier is applied to the experimental and analytical data to estimate the degree of damage in the RC beams. The analytical resonant frequencies show good agreement with the experimental results of local through-thickness vibration tests on the damaged RC beam specimens. In addition to the analysis, the cracks in the RC beams are well detected by machine learning. This study shows that the proposed crack model is effective for crack identification in local vibration testing. Furthermore, machine learning contributed to improving the accuracy of damage detection.