High-Resolution Tandem Mass Spectrometry of complex mixtures with a Multiple-Reflection Time-of-Flight Mass-Spectrometer
Bibliographic record
Abstract
In this work, a multiple-reflection time-of-flight mass spectrometer (MR-TOF-MS) was adapted and employed, for the first time, for ultra-high resolution analytical tandem mass spectrometry. The device was deployed to analyze isobaric molecules in a heavy crude oil sample and to derive new structure formulas thanks to the device's ultra-high separation power and advanced data analysis techniques. A new atmospheric pressure interface (API) was built to operate multiple ion sources in parallel and to optimize the transmission for viscous samples, like crude oil residue. A radio-frequency carpet funnels the ions to the RFQ transport line, providing a compact, efficient, and fast method to combine ions from different sources in parallel to introduce calibrants, while avoiding chemical reactions with the analyte. Ion sources of three different types, nano-ESI, micro-ESI, and thermal 133Cs, were designed, built, coupled to the API, and operated to acquire the data shown in this thesis. A software package was developed for operating the device and for acquiring and analyzing mass spectra. The software allows automatically setting up the measurement by converting parameters like the desired mass range to voltages and HV pulse sequences in real time. Further automated measurement features are a time-of-flight drift correction, a scanning of measurement parameters, and an automatic gain control. In addition, its calibration technique and its enhanced elemental composition identification further assist in analysis. Furthermore, data analysis procedures were developed to increase the mass range, while retaining a high mass accuracy and mass resolving power for closed-path MR-TOF-MS. The software is also used together with MR-TOF-MS devices at particle accelerators, i.e. the MR-TOF-MS of the FRS Ion Catcher at GSI, Darmstadt, and the MR-TOF-MS of the TITAN experiment at TRIUMF in Vancouver. The device in this work features a mass resolving power (FWHM) of up to 300000, a sensitivity down to 10 mol/l, a linear dynamic range of five orders of magnitude, and mass accuracy of 0.3 ppm. Its re-trapping technique allows for a precursor mass separation power of up to 250000 at an efficiency of 10 % or up to 100000 with an efficiency of 80 % for tandem mass spectrometry. These capabilities were shown for tandem mass spectrometry measurements up to the fourth stage (MS^4). Furthermore, molecules as heavy as human insulin (5810 u) have been analyzed. Tandem mass spectrometry of a sample of medium-heavy crude oil residue was performed and four isobaric mass signals close to 322.2 u/e were isolated with an isolation window of 10 mu and a suppression factor of 200. This was only possible thanks to this device's ultra-high mass separation power. The resulting fragments within a mass range of 300 - 500 u were measured at a mass resolving power of 200000 and a mass accuracy of better than 1 ppm, and their elemental compositions were identified. With the help of our collaboration with the workgroup Mass Spectrometry of Prof. Dr. Wolfgang Schrader at the Max Planck Institute für Kohleforschung in Mülheim an der Ruhr, 26 molecular structures for the two isobaric precursor molecules, C23H32N+ and C23H28ON+, were identified. For future applications of the device, investigation of less understood crude oil samples as well as applications in life sciences are envisioned.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".