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). \n1.\tThe 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. \n2.\tThe 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".