Where have all the ions gone, long time passing? Tandem quadrupole mass spectrometers with atmospheric pressure ionization sensitivity gains since the mid‐1970s. A perspective
Bibliographic record
Abstract
The gains in sensitivity since 1975 for quadrupole mass spectrometers equipped with atmospheric pressure ionization (API), and in particular triple quadrupole mass spectrometers (QqQs) since 1981, have been driven by the needs of the environmental, biomedical, agricultural, and other scientific research, industrial, regulatory, legal, and sporting communities to continually achieve lower limits of quantitation and identification. QqQs have realized a one-million-fold improvement in sensitivity attempting to address these needs over the past two score years. It is the purpose of this article to describe how that came about, not through an exhaustive review of the literature, but rather by describing what general approaches were used across the industry to improve sensitivity and provide some examples to illustrate its evolution. The majority of the gains came from the ion source and its interface to the vacuum system. "Sampling efficiency" is a measurement of the losses in this area so will be a focus of this review. The discovery of the phenomenon of collisional focusing was key to improving sampling efficiency because it enabled designs that increased the ion-containing gas loads from the ion source, using staged differential pumping backed by increasingly larger pumps, and prevented the scattering losses of ions in the resulting gas expansion inside vacuum. Likewise, systems with smaller pumps and lower ion-containing gas loads could be designed with size and cost reduction in mind while maintaining reasonable sampling efficiencies. As a consequence, advancements in the designs of both larger and smaller turbomolecular vacuum pumps were accelerated by pump manufacturers to accommodate the explosive growth in the use of API-QqQ and API-ion trap mass spectrometers that occurred in the 1990s and continued into the new millennium. Sampling efficiency was further improved by increasing the ion yield from electrospray by increasing the rate of droplet desolvation. An estimate of the practical limit to further sensitivity improvements beyond what has been achieved to date is provided to shed light on what to expect in the future. Lastly, the implications and unforeseen consequences of the sensitivity gains are considered with a particular focus on how they have enabled a dramatic increase in daily sample throughput on triple quadrupole and other types of mass spectrometers.
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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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.007 | 0.018 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".