Key Takeaways & Executive Findings
- •• • Achieved near 100% accurate identification of seven PFCA isomers using engineered Aerolysin nanopores (WT, R220N, R220Q), demonstrating the capability to resolve structural isomers with minimal differences. • • The method operates without standard reference compounds, overcoming a major bottleneck in conventional PFAS analysis, which is critical for nontargeted screening and rapid risk assessment. • • Feature selection enabled high classification accuracy even with low data volumes, reducing computational and experimental burden, which is essential for real-time environmental monitoring. • • The study establishes a linear relationship between molecular volume and current blockade, providing a quantitative basis for standard-free analysis of PFCAs, with potential extension to other persistent organic pollutants.
Abstract
Per- and polyfluoroalkyl carboxylic acids (PFCAs) are persistent organic pollutants whose isomers exhibit distinct environmental behaviors, bioaccumulation potentials, and toxic effects due to structural variations. Accurate identification of PFCA isomers is critical for risk assessment and pollution control, yet existing detection methods predominantly rely on standard references, posing challenges for precise analysis of isomers with subtle structural differences. Single-molecule electrochemical sensing via nanopores offers a standard-free approach by correlating molecular volume with current blockade, but its capability to distinguish PFCA isomers remained unverified. This study targeted three sets of PFCA isomers: 4,5,5-trifluoropent-4-enoic acid vs. 4,4,4-trifluoro-3-methylbut-2-enoic acid; 3,3,3-trifluoro-2-methylpropanoic acid vs. 4,4,4-trifluorobutanoic acid; and 2-(trifluoromethoxy)acetic acid, 3,3,3-trifluorolactic acid, and (2R)-3,3,3-trifluoro-2-hydroxypropanoic acid. By engineering nanopore interfaces (WT, R220N, R220Q Aerolysin) and extracting multi-dimensional characteristic parameters, the method achieved near 100% accuracy in identifying all seven isomers. Feature selection further enabled high classification accuracy with low data volumes, laying the foundation for rapid single-molecule detection of PFAS and other emerging contaminants.
1. Introduction
Per- and polyfluoroalkyl substances (PFAS) are ubiquitous environmental contaminants, and their carboxylic acid derivatives (PFCAs) pose significant risks due to persistence and toxicity. However, PFCA isomers, which differ only in the position of fluorine atoms or functional groups, exhibit vastly different environmental fates and toxicological profiles. Conventional analytical techniques, such as liquid chromatography-mass spectrometry, require authentic standards for each isomer, which are often unavailable or costly, impeding comprehensive risk assessment. This bottleneck has driven the search for standard-free, high-resolution detection methods.
Nanopore-based single-molecule electrochemical sensing offers a promising alternative by transducing molecular characteristics into ionic current blockades. Prior work established a linear correlation between molecular volume and current blockade, enabling quantification of linear PFCAs without standards. Yet, the ability to discriminate isomers with nearly identical volumes remained unexplored. This study addresses that gap by systematically engineering Aerolysin nanopore interfaces and employing multi-dimensional feature extraction combined with machine learning. The approach achieves near-perfect classification of seven PFCA isomers, demonstrating that nanopore sensing can resolve subtle structural differences, thereby providing a rapid, standard-free platform for PFAS isomer analysis and potentially other emerging contaminants.
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TANG Wen, LI Hongshuang, ZHAO Xian, CHENG Mengyuan, LI Pufeng, XIE Xueying, ZUO Jiaqi, QIU Kaipei (2026). Single-Molecule Electrochemical Analysis of Per- and Polyfluoroalkyl Carboxylic Acid Isomers. Environmental Chemistry. https://doi.org/10.7524/j.issn.0254-6108.2025011804
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Frequently Asked Questions
What is the limit of detection for PFCA isomers using this nanopore method?
The study reports near 100% classification accuracy for seven isomers, but specific LOD values are not provided in the abstract. However, single-molecule detection inherently offers ultra-low detection limits, typically at the nanomolar to picomolar range, depending on the nanopore and analyte. Further details would require accessing the full paper.
How does the method handle complex environmental matrices with interfering substances?
The study focuses on pure isomer mixtures, but the use of engineered nanopores (WT, R220N, R220Q) and multi-dimensional feature extraction may enhance selectivity. In real samples, sample preparation steps such as solid-phase extraction would likely be needed to concentrate PFCAs and remove interferences. The method's robustness in complex matrices is not explicitly addressed in the abstract.
What is the throughput of this single-molecule technique compared to conventional LC-MS/MS?
Single-molecule nanopore sensing is inherently low-throughput, typically analyzing one molecule at a time. However, the method offers rapid detection (milliseconds per event) and does not require chromatographic separation, potentially reducing total analysis time. For routine monitoring, parallel arrays of nanopores could increase throughput, but this is not discussed in the abstract.
Can this method be extended to other PFAS classes beyond carboxylic acids?
The principle of correlating molecular volume with current blockade is general, but the nanopore interface and feature extraction may need optimization for different functional groups. The study demonstrates feasibility for PFCAs, and with further engineering, it could be adapted to sulfonates, sulfonamides, and other PFAS subclasses.
What is the reproducibility and stability of the engineered nanopores?
The abstract does not provide specific data on reproducibility or stability. However, Aerolysin nanopores are known for their robustness, and the use of multiple mutants (R220N, R220Q) suggests systematic optimization. Long-term stability and batch-to-batch variability would need to be assessed for practical applications.
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