Chemical Ionization Mass Spectrometry: Fundamental Principles, Diverse Applications, and the Latest Technological Frontiers
Bibliographic record
Abstract
The review examines the evolution of chemical ionization mass spectrometry (CI-MS), a technique developed in 1966 by Field and Munson. CI is a soft-ionization method that produces more intense molecular ions with less fragmentation than electron ionization (EI). CI-MS is widely utilized across various fields, including atmospheric chemistry, environmental science, and biomedical research. The article highlights different CI-MS types, such as proton transfer reaction mass spectrometry (PTR-MS), which is renowned for its ability to analyze volatile organic compounds in real-time; negative ion CI-MS, which provides insights into anions; selected ion flow tube mass spectrometry (SIFT-MS), and ion-drift chemical ionization mass spectrometry (ID-CIMS), techniques that allow for the direct analysis of trace gases with high sensitivity and specificity. The article discusses advancements in chromatography with CI-MS, particularly atmospheric pressure chemical ionization (APCI) and liquid electron ionization (LEI) interface. The ongoing exchange of data between fundamental ion/molecule studies and specific applications has significantly boosted the growth of CI-MS in recent decades. In recent years, no extensive review has been published on CI-MS. This article provides an overview of CI-MS technique, its applications, and its evolution over the years, highlighting its importance in advancing scientific research and understanding the chemistry of various environments.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".