Use of Laser Light Scatter Signals to Study the Effect of a Direct-Current Biased Mesh Screen in a Spray Chamber on Aerosols Generated for Use in Atomic Spectroscopy
Bibliographic record
Abstract
A new method that relies on tertiary aerosol laser light scatter signals to alleviate the NaCl chemical matrix effect in inductively coupled plasma atomic emission spectroscopy (ICP-AES) is introduced. The method involves monitoring the laser light scatter signal from the entire tertiary aerosol with a line-shaped laser beam and the plasma off. In this work, the tertiary aerosol laser scatter signal was measured to increase nonlinearly with NaCl concentration. The source of the increased scatter signal is described as originating from Coulomb fission events that droplets with net charge experience while inside the spray chamber. Each droplet in the secondary aerosol that undergoes Coulomb fission introduces ∼ 20 progeny droplets into the aerosol stream. The introduction of progeny droplets to the aerosol stream measured by increased laser light scatter signals coincides with an increase in analyte emission, off-axis, at low viewing heights in an inductively coupled plasma, a characteristic of the easily ionized element chemical matrix effect. It is shown that a direct-current potential applied to a mesh screen positioned inside the spray chamber can coarsely control the transport of droplets with net charge through the spray chamber, by effecting their removal from the aerosol stream. With an appropriate voltage applied to the mesh, the laser scatter signal from the tertiary aerosol from a solution containing 100 mM NaCl can be made similar to the scatter signal from a solution that does not contain NaCl. Under the same conditions, calcium-ion lateral emission signal intensity contour maps in a plasma for the same two solutions become similar within the normal analytical zone in the plasma (2 mm wide, from 13 to 19 mm above the load coil).
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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 | 0.001 |
| 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".