Improvements to Single Particle ICPMS by the Online Coupling of Ion Exchange Resins
Bibliographic record
Abstract
Single particle ICPMS (SP-ICPMS) is becoming a very promising technique for nanoparticle detection and characterization, especially at very low concentrations (~10(-12)-10(-10) M). Nonetheless, the ability of the technique to detect smaller nanoparticles is presently limited by the setting of threshold values for the discrimination of nanoparticles from the dissolved metal background. In this study, a new approach to attaining lower particle size detection limits has been developed by the online coupling of an ion exchange column (IEC) with SP-ICPMS (IEC-SP-ICPMS). The IEC effectively removes the continuous signal of dissolved metal, allowing for both lower detection limits and an improved resolution of solutions containing multiple particles. The feasibility and the efficiency of this coupling were investigated using silver nanoparticles in the presence of various concentrations of Ag(+) and other major ions (Mg(2+), Ca(2+), Na(+), K(+), and Cl(-)). The online elimination of the dissolved metal made data processing simpler and more accurate. Following the addition of 1 to 4 μg L(-1) of Ag(+) spikes, symmetric particle size distributions were obtained using IEC-SP-ICPMS, whereas the use of SP-ICPMS alone led to asymmetric distributions, especially for nanoparticle sizes below 60 nm. Although this proof of principle study focused on nanosilver, the technique should be particularly useful for any of the metal based nanoparticles with high solubilities.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
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".