Flow Injection Single Particle Inductively Coupled Plasma Mass Spectrometry: An Original Simple Approach for the Characterization of Metal-Based Nanoparticles
Bibliographic record
Abstract
In recent years, single-particle inductively coupled plasma mass spectrometry (spICPMS) has emerged as a reliable tool that can both count metal-containing nanoparticles and measure their mass, thereby allowing sizing if their shape, density, and composition are known. However, the methodology associated with the current spICPMS approach for mass determination requires determination of both the sample uptake rate and the sample introduction efficiency of the nebulization system. In this paper, the proof of concept of a novel approach based on flow injection (FI) analysis coupled to ICPMS, i.e., FI-spICPMS, is presented. Unlike the established technique, this method does not require a determination of the transport efficiency and of the sample uptake rate for the accurate measurement of particle mass. It also only requires a measurement of the transport efficiency for determination of the particle number. Unlike the traditional spICPMS approach, the measurement of transport efficiency by FI-spICPMS is not affected by changes in sample uptake rate. The efficiency of FI-spICPMS is demonstrated through accurate determination of the particle number and size of 60 nm citrate-coated gold nanoparticles suspended in high-purity water. Despite being simpler, the method provides similar results to those obtained by the established spICPMS method. With a 5 ms dwell time and 200 μs settling time, the size detection limit is 20 nm, i.e., the same as with spICPMS.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".