Interpreting data from catalytic studies is often complicated by the complexity of the reactions and the materials involved. Techniques like microcalorimetry and SERS generate vast amounts of data that require sophisticated algorithms for analysis. The presence of noise and background signals can further complicate the extraction of meaningful information. Additionally, the need for advanced computational methods to simulate and interpret data adds another layer of complexity. Misinterpretation of data can lead to incorrect conclusions about catalytic mechanisms and efficiencies.
