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A new computational method, IMPACT4-CCS, accurately predicts collision cross section values for per- and polyfluoroalkyl substances (PFAS). This approach enhances PFAS identification and classification, even for complex emerging subclasses, by integrating ab initio and machine learning techniques.

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PFAScollision cross sectionmachine learningmass spectrometry

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Area of Science:

  • Environmental Chemistry
  • Computational Chemistry
  • Analytical Chemistry

Background:

  • Collision cross section (CCS) is crucial for identifying and classifying per- and polyfluoroalkyl substances (PFAS).
  • Existing computational methods face challenges with large molecules, increasing PFAS candidates, and generalizing machine learning models to diverse structures.
  • Accurate and timely CCS prediction is essential for environmental monitoring and risk assessment of PFAS.

Purpose of the Study:

  • To develop a novel computational workflow ensemble, IMPACT4-CCS, for accelerating accurate CCS prediction of PFAS molecules.
  • To improve the accuracy and generalizability of CCS prediction, particularly for emerging PFAS subclasses and flexible molecular structures.
  • To demonstrate the advantage of integrating ab initio methods with machine learning over purely data-driven approaches for CCS analysis.

Main Methods:

  • Developed IMPACT4-CCS, a computational workflow combining ab initio calculations with machine learning.
  • Validated the method's accuracy against a test set of 100 molecules.
  • Assessed performance on specific PFAS subclasses, including nH-perfluoroalkyl carboxylic acids (nH-PFCA), and evaluated the capability to capture structural dynamics like hydrogen bridging.

Main Results:

  • IMPACT4-CCS achieves accuracy comparable to current machine learning approaches.
  • The method shows superior accuracy for emerging PFAS subclasses, such as nH-PFCA, outperforming other methods that overestimate CCS values.
  • IMPACT4-CCS is the first method demonstrated to capture structural dynamics (hydrogen bridging) in large, flexible PFAS molecules.

Conclusions:

  • IMPACT4-CCS offers a significant advancement in the accurate and efficient prediction of CCS for PFAS.
  • Integrating machine learning with traditional computational methods provides greater accuracy and generalizability than relying solely on machine learning on molecular graphs.
  • The developed workflow has potential applications in expanding nontarget analysis for vast PFAS datasets, such as the OECD PFAS list.