Improvement of the capabilities of inductively coupled plasma optical emission spectrometry for the analysis of complex matrices and for single particle analysis
Bibliographic record
Abstract
The objective of this thesis is to explore new applications of inductively coupled plasma optical emission spectrometry (ICP-OES). 1. The first application involved the development of a method for the direct bulk analysis of a 12 M KOH zincate electrolyte fuel, which is used for green energy backup systems. By using flow injection analysis in combination with an inert sample introduction system, the concentrations of additives (Al, Fe, Mg, In, Si) and corrosion products (Zn2+ and CO32-) were quantified. However, accurate determination of all elemental concentrations was unsuccessful due to suppression from the matrix. 2. The second application focused on exploring and enhancing the capabilities of single particle ICP-OES analysis for the characterization of the particles filtered from the 12 M KOH zincate electrolyte fuel. A conventional pure argon plasma and Ar-N2-N2/H2 mixed gas plasma were compared with regards to sensitivity, detection limit, and robustness to establish which operating conditions minimize the detectable particle mass. The effect of infrared heating the sample aerosol in the spray chamber and base of the torch was explored to increase transport efficiency and reduce the noise arising from aerosol processing within the plasma that would degrade detectable particle mass. Two surfactants were also explored to stabilize and disperse particles in solution.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".