Work

Machine learning assisted single-molecule sensing towards standard-free quantification of per- and polyfluoroalkyl carboxylic acids

Year: 2026

Type: article

Abstract: Per- and polyfluoroalkyl carboxylic acids (PFCA) are of great concern due to their ubiquitous presence in the environment. Despite a severe shortage of authentic standards, compared to the rapid increase of possible structures identified, it remains difficult to quantify a mixture of PFCA without references. Herein, a standard-free single-molecule electrochemical sensing method was developed for the first time by establishing a linear correlation between current blockades and the volumes of PFCA simulated by molecular dynamics. A nearly 100% accuracy was realized for the simultaneous determination of 13 pristine or H- / Cl-substituted PFCA, using frequency-modulated multi-feature classification. Shortlisting the 21 high-priority features reduced the required number of training data by 7.6 folds, and almost 80% quantification reliability was maintained even with interference of 100 times concentration. Moreover, the detection limit of trifluoroacetic acid (an ultrashort-chain PFCA) went down to 57 ng·L -1 , comparable to the state-of-the-art performance.

Source: Nature Communications

DOI: 10.1038/s41467-026-70718-3