New Breakthrough in Single-Molecule Environmental Analytical Chemistry from ECUST Published in Nature Communications

Recently, a breakthrough was achieved in the field of “Artificial Intelligence + Single-Molecule Environmental Analytical Chemistry” by the research group led by Associate Professor Kaipei Qiu from the Key Laboratory of Environmental Risk Assessment and Control on Chemical Process, Ministry of Ecology and Environment, School of Resources and Environmental Engineering, ECUST. 

The team achieved standard-free quantification of ultra-trace per- and polyfluoroalkyl carboxylic acids (PFCAs) in complex environmental matrices. The relevant research findings were published in Nature Communications under the title “Machine learning assisted single-molecule sensing towards standard-free quantification of per- and polyfluoroalkyl carboxylic acids”.

Per- and polyfluoroalkyl substances (PFAS) represent a class of emerging contaminants of major concern. To date, more than 14,000 PFAS with identified structures have been recognized, and quantitative analysis of the occurrence characteristics of PFAS in various environmental matrices is crucial for elucidating their migration and transformation mechanisms as well as ecological and health risks. 

Due to the extreme scarcity of commercial reference standards (less than 1% of the total), targeted detection techniques such as chromatography-mass spectrometry face challenges in achieving full-coverage analysis of PFAS, while the quantitative accuracy of non-targeted screening techniques represented by high-resolution mass spectrometry remains to be verified.

Nanopore-based single-molecule electrochemical sensor enables high-fidelity recording of structural information of analyte molecules passing through the nanopore (from “information” to “signal”) by virtue of ultra-high structural resolution brought by near-field signal transduction. 

Combined with multi-dimensional feature classification algorithms (from “signal” to “information”), precise identification of individual PFAS molecules passing through the nanopore can be achieved. By calculating the translocation frequency of corresponding molecules, simultaneous quantitative analysis of multiple ultra-trace PFAS homologues and even isomers can be realized even in the presence of a large number of interfering substances with drastically different concentrations and minimal structural differences in complex environmental matrices.

Based on the above strategy, the team utilized an aerolysin nanopore system to study the standard-free detection of per- and polyfluoroalkyl carboxylic acids (PFCAs) guided by oligo-arginine chains. By establishing a linear relationship between current blockades and molecular volumes for PFCAs and combining feature engineering with machine learning algorithms, 100% accurate identification of 13 PFCAs was achieved. 

Further, through molecular dynamics simulation-guided nanopore design and enhanced probe-driven capture strategies, a universal probe-determined calibration curve covering nearly half of PFCAs was experimentally realized, with the detection limit for trifluoroacetic acid (an ultrashort-chain PFCA) reduced to 0.1 nM (10 ng/L). The double-barriers of probe-pore interaction suggest that capture rates can be independently tuned without compromising identification.

Finally, anti-interference tests in different environmental matrices (including tap water, serum, and various coexisting pollutants such as fatty acids, antibiotics, and heavy metal ions) showed that the qualitative and quantitative capabilities of single-molecule analysis were unaffected. 

Relevant recent studies by the research group have further expanded standard-free qualitative analysis to 10 categories and more than 80 PFCAs, and standard-free quantitative analysis to cover all PFCAs with a minimum detection limit down to 1 pM, laying a foundation for the development of on-site rapid detection technologies for PFAS.

ECUST is the sole corresponding affiliation of the paper, and Associate Professor Kaipei Qiu from the School of Resources and Environmental Engineering is the corresponding author. Master’s graduates Jiaqi Zuo, Hong-Shuang Li and Wen Tang are the co-first authors. This research was supported by the National Natural Science Foundation of China, the National Key R&D Program of China, the Natural Science Foundation of Shanghai Municipality.