Bibliographic record
Abstract
A clear understanding of small organic molecules (SOM) electrochemical oxidation opens a great opportunity for breakthrough in the development of fuel cell technology. SOM such as formic acid, methanol, and ethanol can produce electrical power through their oxidation in the fuel cell's anode. These molecules are also known as organic fuels and theoretically have the potential to produce close to 100% energy efficiency in a fuel cell. However, fast and complete oxidation of some organic fuels, such as ethanol, has not been achieved at this time, and has led to a dramatic decrease in the level of fuel cell efficiency. Therefore, a comprehensive study of the electrocatalytic oxidation mechanisms of organic fuels as well as a determination of the average number of transferred electrons (nₐᵥ) are crucial for the enhancement of fuel cell efficiency. Hydrodynamic methods are highly effective approaches for these study purposes, and they have the ability to emulate the hydrodynamic conditions of a fuel cell anode. The main purpose of this project was establishing a simple and novel system for the assessment of various fuel cell catalysts performances in relation to formic acid, methanol and ethanol electrochemical oxidation. For this purpose, we applied two different approaches of hydrodynamic techniques, rotating disk voltammetry (RDV) and ow cell electrolysis. Also, as for fuel cells, thick catalyst layers were applied in our studies in order to obtain meaningful data which are more relevant to an actual fuel cell. We showed that RDV is a convenient and useful method for the determination of the pure kinetic component of the oxidation current which represents a catalyst activity. Also, we estimated nav for methanol and ethanol using mathematical treatments related to RDV. Two-electrode and three-electrode ow-through cells were designed to determine the mass transport and kinetic parameters of the formic acid oxidation current, which can be further extended to methanol and ethanol for nₐᵥ determination. The two-electrode ow-through cell provided for rapid collection of oxidation products and real time measurements of CO₂ for stoichiometric investigations.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".