Method for Studying High Temperature Aqueous Electrochemical Systems: A Self Pressurized Autoclave
Bibliographic record
Abstract
Understanding oxidation kinetics and reaction mechanism at elevated temperatures is paramount to making efficient fuel cells that can run at temperatures higher than 100oC. We describe a method that allows for studies of aqueous electrochemistry at temperatures above the normal boiling point of water, and this system is used to study potential fuels for direct fuel fed fuel cells. Two alcohols with very different partial pressures at temperatures above 100oC are used in this study: methanol and glycerol. A self-pressurized glass autoclave heated in an oil bath is used for this purpose, and a model of the autoclave is shown in Fig. 1a. Slow ramping of the temperature allows for efficient data acquisition by using cyclic voltammetry up to 140oC, exemplified by Fig. 1b, and kinetic parameters such as onset potentials, Tafel slopes and activation energies are found and discussed for the relevant fuels. For methanol oxidation, dissociative water adsorption plays a key limiting role in the total reaction of methanol to CO2 at all temperatures. For glycerol oxidation, some partial oxidation products do not require an oxygen donor in the form of adsorbed water for their formation. This is deemed to play a fundamental role giving a large drop in overpotential at temperatures above 110oC, and making glycerol a potential fuel cell candidate at these temperatures. Therefore, a partial oxidation of glycerol in a fuel cell offers a potential method for a combined fuel cell and value-adding process for abundant glycerol. 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".