Nanomaterials and nanostructures often exhibit unique properties that are highly sensitive to slight variations in their environment or composition. Inaccurate or dirty data can lead to erroneous conclusions, flawed models, and potentially costly mistakes. Therefore, a rigorous data cleaning process is essential to ensure the validity of experimental results and computational models.
