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This study introduces a machine learning calibration method to measure ion-neutral collision cross sections (CCSs) using differential mobility spectrometry (DMS). This approach accurately determines CCSs, overcoming previous limitations in non-linear IMS techniques.

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Area of Science:

  • Analytical Chemistry
  • Physical Chemistry
  • Computational Chemistry

Background:

  • Experimental determination of ion-neutral collision cross sections (CCSs) is typically limited to specific ion mobility spectrometry (IMS) techniques.
  • Correlating ion trajectories to CCS in dynamic electric field IMS methods like differential mobility spectrometry (DMS) has been challenging.

Purpose of the Study:

  • To develop and validate a machine learning (ML)-based calibration strategy for measuring CCSs using DMS.
  • To enable accurate CCS determination in non-linear IMS techniques.

Main Methods:

  • Trained a machine learning framework using differential mobility data from 409 molecular cations in a N2 environment.
  • Utilized DMS-MS data, including parent ion m/z and compensation voltage, at separation voltages from 1500 to 4000 V.
  • Evaluated several open-source ML routines, with random forest regression showing the best performance.

Main Results:

  • The random forest regression model predicted CCSs with a mean absolute percent error of 2.6 ± 0.4% for external validation data.
  • Achieved accuracy comparable to MobCal-MPI CCS calculations (∼2.2% error) and TWIMS platform thresholds (<2% error).
  • Demonstrated the feasibility of measuring CCSs for a wide range of molecular cations (m/z: 86–683 Da, CCS: 110–236 Ų).

Conclusions:

  • Machine learning calibration effectively enables the measurement of ion-neutral collision cross sections (CCSs) from differential mobility spectrometry (DMS) data.
  • This ML-based approach overcomes previous challenges in correlating ion behavior to CCS in dynamic electric field IMS.
  • The validated method offers a promising avenue for accurate CCS determination across various molecular ions.