(General Student Poster Session Winner - 2nd Place) Electrolysis Reference Electrode Methodology, and Electrochemical Modelling Applications
Bibliographic record
Abstract
The added constraints of fuel cells and electrolysers can make incorporating reference electrodes into their design a complex and invasive process. One set of solutions to this problem includes placing multiple reference electrodes around the perimeter to be the least invasive as possible, adding a catalyst patch to the membrane to keep potentials from fluctuating, and using additional hydrogen producing electrodes as a reference for potential [1]. The referenced approach has been proven to work in fuel cells; this work seeks to apply the referenced methodology to electrolysers, and expand upon it using electrochemical modelling techniques. By characterizing the net- and half-reactions using polarization curves and Tafel analysis at a series of different temperatures, it is possible to model the half-reaction parameters from polarization curve data collected for the net-reaction. The resulting model will then enable estimates of half-reaction parameters in a conventional two-electrode electrolyser setup. Figure 1 shows the suggested set-up and reference electrode placement for this experiment. The reference electrodes; Re1, Re2, Re4 and Re5 will be platinized platinum wires and will work as Reversible Hydrogen Electrodes in this system. Reference electrodes ReP,0 and ReP,3 will be catalyst patches placed in chambers where the conditions will be held constant. Sensing electrodes will also be added as patches but are not subject to the same conditions as the reference electrodes. The conditions, temperature, pressure and humidity, in the cell will be known, thereby allowing for the characterization and correction of the reference electrode potentials against the Standard Hydrogen Electrode. References: Herrera, O. et al., “New Reference Electrode Approach for Fuel Cell Performance Evaluation” ECS Trans. Vol. 16, p. 1915, (2008) Figure 1
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.330 | 0.244 |
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".