The accurate detection of per- and polyfluoroalkyl substances (PFAS), often termed “forever chemicals,” presents a critical and complex challenge for environmental science due to their structural diversity, the lack of standardized methods, and the need for highly sensitive equipment to measure trace environmental levels. A study published in Environmental Science and Technology reveals an innovative, deep learning-based approach to overcome these obstacles. The prevalence of background contamination and the sheer number of distinct PFAS compounds further complicate the development of universal detection protocols.