Trace level impurity method development with high‐field asymmetric waveform ion mobility spectrometry: systematic study of factors affecting the performance
Bibliographic record
Abstract
For the determination of trace level impurities, analytical chemists are confronted with complex mixtures and difficult separations. New technologies such as high-field asymmetric waveform ion mobility spectrometry (FAIMS) have been developed to make their work easier; however, efficient method development and troubleshooting can be quite challenging if little prior knowledge of the factors or their settings is available. We present the results of an investigation performed in order to obtain a better understanding of the FAIMS technology. The influence of eight factors (polarity of dispersion voltage, outer bias voltage, total gas flow rate, composition of the carrier gas (e.g. %He), outer electrode temperature, ratio between the temperatures of the inner and outer electrodes, flow rate and composition of the make-up mobile phase) was assessed. Five types of responses were monitored: value of the compensation voltage (CV), intensity, width and asymmetry of the compensation voltage peak, and resolution between two peaks. Three types of studies were performed using different test mixtures and various ionisation modes to assess whether the same conclusions could be drawn across these conditions for a number of different types of compounds. To extract the maximum information from as few experiments as possible, a Design of Experiment (DoE) approach was used. The results presented in this work provide detailed information on the factors affecting FAIMS separations and therefore should enable the user to troubleshoot more effectively and to develop efficient methods.
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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 | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".