University of Alberta An Electrochemical Impedance Spectroscopic Diagnostic Device for Characterization of Liquid-Liquid Systems and Phase Separation Detection in
Bibliographic record
Abstract
Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of these terms. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author’s prior written permission. I would like to share the moment of accomplishing my MSc years with my mom and dad, who have always been such great supports for me, and with my dear wife, Azin, my source of inspiration. Rapid characterization of complex fluids, especially sensing emulsion stability, is crucial for many industrial applications, ranging from pharmaceutical industry to petroleum pro-duction. Electrochemical impedance spectroscopy (EIS) is a powerful tool for electrical characterization of such systems. In this study, a “milli-fluidic ” EIS test fixture which is inexpensive, easily fabricated, yet robust, is designed and fabricated using 3D printing technology, which allows reliable experiments, and can then be disposed of after each test. The developed cell is tested using solutions, liquid-liquid mixtures, and oil-water emul-sions. Frequency response analysis and equivalent circuit modeling have been performed to find the effective electrical properties of the liquids, and relate them to their physical properties, such as stability. EIS is applied to reveal the electrical behavior of emulsions during phase separation. It is found that creaming can be sensed based on permittivity decay, and capacitance-based measurements can be utilized to detect phase separation in emulsions.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.009 |
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".