Catalysis experiments often generate large datasets with inherent noise due to experimental conditions, measurement errors, or fluctuations in reaction parameters. Data smoothing helps in:
Enhancing the signal-to-noise ratio in the data Identifying underlying trends and patterns Improving the accuracy of model fitting and parameter estimation Facilitating better decision-making in catalyst design and optimization
