Single particle inductively coupled plasma mass spectrometry and its variations for the analysis of nanoparticles
Bibliographic record
Abstract
Single particle inductively coupled plasma mass spectrometry (spICPMS) can count and weigh metal-containing nanoparticles (NPs), enabling their sizing if their geometry, density, and composition are known. With a nebulizer and a spray chamber for sample introduction, both the sample uptake rate and the transport efficiency must be determined when calibrating with solutions. In contrast, flow injection (FI) and mono-segmented flow analysis (MSFA) coupled to spICPMS do not need determination of the transport efficiency and sample uptake rate for accurate NP mass measurement. Correcting for the significant settling time on some instruments is also discussed, as well as calibration through signal integration instead of averaging, which eliminates the need to measure the transport efficiency when seeking NP mass. Nitrogen added to the outer plasma gas can reduce the background for the determination of P, S, Ca and Fe. Infrared heating of the sample introduction system provides 100% transport efficiency, enabling accurate particle mass and concentration measurements without measurement of transport efficiency.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".