How soft is your ESI-MS anyway?
Bibliographic record
Abstract
Electrospray ionization mass spectrometry (ESI-MS) is a powerful tool for identifying and characterizing organometallic and coordination compounds. However, detection of fragile structures bound by weaker intermolecular forces can be significantly limited in ESI-MS owing to the use of relatively harsh instrument conditions and configurations. In this study, a set of tests was developed to assess the softness of ESI-MS systems. Two variants are presented: positive ion mode, utilizing a mixture of sodium ions and triphenylphosphine oxide producing [Na(OPPh3)n]+ ions (n = 1-4), and negative ion mode utilizing Pd(PPh3)4 and sulfonated triphenylphosphine producing [Pd(L)(PPh3)n]– ions (n = 0-2), where softer instrument conditions preserve a higher proportion of the high-coordinate ions and harsher conditions will result in increased detection of products of ion fragmentation. The results revealed notable variations in instrument softness, which were influenced by a combination of instrument design and experimental parameters. Meticulously optimizing experimental conditions and ESI-MS parameters is essential to achieving the softest ionization possible, ensuring reliable analysis where applicable. This study offers valuable insight through straightforward tests that can be employed to assess the suitability of an instrument for specific research needs.
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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 | 0.006 | 0.013 |
| 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 | 0.002 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.049 | 0.055 |
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".