Ion suppression : A major concern in mass spectrometry
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Ion suppression is one form of matrix effect that liquid chromatography–mass spectrometry (LC–MS) techniques suffer from, regardless of the sensitivity or selectivity of the mass analyzer used. Ion suppression negatively affects several analytical figures of merit, such as detection capability, precision, and accuracy. The limited knowledge of the origin and mechanism of ion suppression makes this problem difficult to solve in many cases. Over the past decade and a half since the response-reducing phenomenon was exposed, however, protocols have been developed not only to test for its presence but also to account for its effects and eliminate the risk of ion suppression altogether. Because there is no universal solution for the matrix effect, some of the viable options are discussed briefly in this tutorial, which alone or in combination can help regain the quality of LC–MS analysis for the particular matrix–analyte combination. Two commonly used techniques to detect the presence of the matrix effect are illustrated. Modifying instrumental components and parameters, chromatographic separation, and sample preparation are all considered as means of reducing or possibly eliminating ion suppression. A variety of calibration techniques for compensating the effects of the phenomenon also are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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 it