In this study, we examine the potential application of Scientific Argument Mining (SAM) in enhancing scientific literature correction processes. Specifically, we focus on evaluating SAM's effectiveness in assessing the influence of retracted citations on the accuracy of claims and results within the field of nanobiology. Our objectives include creating a novel SAM dataset derived from nanobiology articles, assessing the adaptability of current SAM frameworks to this new dataset, and offering a comprehensive synthesis of the existing SAM guidelines for scientific literature correction practices.