Ammonia decomposition is a promising method to produce high-purity hydrogen. However, this process typically requires precious metals (such as Ru, Pt, etc.) as catalysts to ensure high efficiency at relatively low temperatures. In this study, we propose using several Ni/GdxCe1-xO2-δ catalysts to improve ammonia decomposition performance by adjusting the support properties. We also investigate the underlying mechanism for this enhanced performance. Our results show that Ni/Ce0.8Gd0.2O2-δ at 600 °C can achieve nearly complete ammonia decomposition, resulting in a hydrogen production rate of 2008.9 mmol.g−1.h−1 with minimal decrease over 150 h. Density functional theory calculations reveal that the recombinative desorption of nitrogen is the rate-limiting step of ammonia decomposition over Ni. Our characterizations indicate that Ni/Ce0.8Gd0.2O2-δ exhibits a high concentration of oxygen vacancies, highly dispersed Ni on the surface, and abundant strong basic sites. These properties significantly enhance the associative desorption of N and strengthen the metal support interactions, resulting in high catalytic activity and stability. We anticipate that the mechanism could be applied to designing additional catalysts with high ammonia decomposition performance at relatively low temperatures.

Pansharpening technology is used to acquire a multispectral image with high spatial resolution from a panchromatic (PAN) image and a multispectral (MS) image. The detail injection model is popular for its flexibility. However, the accuracy of the injection gain and the extracted details may greatly influence the quality of the pansharpened image. This paper proposes an adaptive injection model to solve these problems. For detail extraction, we present a Gaussian filter estimation algorithm by exploring the intrinsic character of the MS sensor and convolving the PAN image with the filter to adaptively optimize the details to be consistent with the character of the MS image. For the adaptive injection coefficient, we iteratively adjust the coefficient by balancing the spectral and spatial fidelity. By multiplying the optimized details and injection gain, the final HRMS is obtained with the injection model. The performance of the proposed model is analyzed and a large number of tests are carried out on various satellite datasets. Compared to some advanced pansharpening methods, the results prove that our method can achieve the best fusion quality both subjectively and objectively.