Efficient Production of Green Hydrogen by Ethanol Electrolysis at a PtRhRu Catalyst
Bibliographic record
Abstract
Production of green hydrogen by electrolysis of ethanol is potentially a more efficient technology than water electrolysis because it requires much lower cell potentials. However, separation and valorization of the acetic acid and acetaldehyde byproducts are required, producing greater uncertainty in the cost of hydrogen. Fluctuations in commodity prices also make it difficult to select the most appropriate catalysts and operating conditions. These issues are addressed here by the analysis of electrochemical data and product distributions, over a range of potentials and ethanol concentrations, using a techno-economic framework to estimate the projected cost of hydrogen. For Jan 2025 prices, a minimum cost of 4.5 USD kg –1 was obtained for the production of hydrogen using a PtRhRu catalyst, which is at the high end of a range estimated for water electrolysis. However, a sensitivity analysis shows that a doubling of the price of acetic acid to 1 USD kg –1 would decrease the hydrogen cost to 1.1 USD kg –1 . The stoichiometry for ethanol oxidation has a strong influence on the cost, since it determines the selectivity for hydrogen production (hydrogen:ethanol ratio). Consequently, the PtRhRu catalyst is more efficient than the PtRu catalysts that are generally employed for ethanol electrolysis due to the high yields of acetic acid and CO 2 that it can produce. Overall, the results of the cost of hydrogen estimates and their dependence on the ethanol concentration and cell potential provide a comprehensive view of the economic potential of ethanol electrolysis and framework for optimizing catalysts and operating parameters in response to changing market conditions.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".