A predictive model correlating the properties of a catalyst with its performance would be beneficial for the development, from biomass waste, of new, carbon-supported and Earth-abundant metal oxide catalysts. In this work, the effects of copper and iron oxide crystallite size on the performance of the catalysts in reducing nitrogen oxides, in terms of nitrogen oxide conversion and nitrogen selectivity, are investigated. The catalysts are prepared via the incipient wetness method over activated carbon, derived from palm kernel shells. The surface morphology and particle size distribution are examined via field emission scanning electron microscopy, while crystallite size is determined using the wide-angle X-ray scattering and small-angle X-ray scattering methods. It is revealed that the copper-to-iron ratio affects the crystal phases and size distribution over the carbon support. Catalytic performance is then tested using a packed-bed reactor to investigate the nitrogen oxide conversion and nitrogen selectivity. Departing from chemical characterization, two predictive equations are developed via an artificial neural network technique—one for the prediction of NO_x conversion and another for N₂ selectivity. The model is highly applicable for 250–300 °C operating temperatures, while more data are required for a lower temperature range.

This study utilised a user-centred design approach integrated into the Agile framework to develop Sarawak Cultural Heritage Progressive Web Apps (SCHPWA). The user experience (UX) of SCHPWA was evaluated using a combination of the User Experience Questionnaire (UEQ) and Importance-Performance Analysis (IPA) to measure six dimensions of UX. The evaluation involved 239 participants, comprising 184 females and 55 males. The results indicated that the SCHPWA scored above average in attractiveness, perspicuity, efficiency, and stimulation. However, the dimensions of novelty and dependability scored below average. Therefore, the attributes related to attractiveness, efficiency, stimulation, and perspicuity are grouped using IPA under Quadrant 2 (Possible Overkill), while novelty and dependability fall under Quadrant 3 (Low Priority). The study highlights the benefits of using a combination of UEQ and IPA for evaluating Progressive Web Applications (PWA). Furthermore, it suggests the potential to improve the UX of cultural heritage PWAs. These findings offer insights for designers and developers in optimising the UX of cultural heritage PWA.