Online aerosol mass spectrometry of single micrometer‐sized particles containing poly(ethylene glycol)
Bibliographic record
Abstract
The analysis of poly(ethylene glycol) (PEG)-containing particles by online single particle aerosol mass spectrometers equipped with laser desorption/ionization (LDI) is reported. We demonstrate that PEG-containing particles are useful in the development of aerosol mass spectrometers because of their ease of preparation, low cost, and inherently recognizable mass spectra. Solutions containing millimolar quantities of PEGs were nebulized and, after drying, the resultant micrometer-sized PEG-containing particles were sampled. LDI (266 nm) of particles containing NaCl and PEG molecules of average molecular weight<500 Da generated mass spectra reminiscent of mass spectra of PEG collected by other mass spectrometer platforms including the characteristic distribution of positive ions (Na+ adducts) separated by the 44 m/z units of the ethylene oxide units separating each degree of polymerization. PEGs of average molecular weight>500 Da were detected from particles that also contained the tripeptide tyrosine-tyrosine-tyrosine or 2,5-dihydroxybenzoic acid, which were added to nebulized solutions to act as matrices to assist LDI using pulsed 266 nm and 355 nm lasers, respectively. Experiments were performed on two aerosol mass spectrometers, one reflectron and one linear, that each utilize two time-of-flight mass analyzers to detect positive and negative ions created from a single particle. PEG-containing particles are currently being employed in the optimization of our bioaerosol mass spectrometers for the application of measurements of complex biological samples, including human effluents, and we recommend that the same strategies will be of great utility to the development of any online aerosol LDI mass spectrometer platform.
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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 | 0.000 | 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) | 0.001 | 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".