Toward enhanced hydrogen generation from water using oxygen permeating LCF membranes
Bibliographic record
Abstract
Hydrogen production from water thermolysis can be enhanced by the use of perovskite-type mixed ionic and electronic conducting (MIEC) membranes, through which oxygen permeation is driven by a chemical potential gradient. In this work, water thermolysis experiments were performed using 0.9 mm thick La0.9Ca0.1FeO3-δ (LCF-91) perovskite membranes at 990 °C in a lab-scale button-cell reactor. We examined the effects of the operating conditions such as the gas species concentrations and flow rates on the feed and sweep sides on the water thermolysis rate and oxygen flux. A single step reaction mechanism is proposed for surface reactions, and three-resistance permeation models are derived. Results show that water thermolysis is facilitated by the LCF-91 membrane especially when a fuel is added to the sweep gas. Increasing the gas flow rate and water concentration on the feed side or the hydrogen concentration on the sweep side enhances the hydrogen production rate. In this work, hydrogen is used as the fuel by construction, so that a single-step surface reaction mechanism can be developed and water thermolysis rate parameters can be derived. Both surface reaction rate parameters for oxygen incorporation/dissociation and hydrogen-oxygen reactions are fitted at 990 °C. We compare the oxygen fluxes in water thermolysis and air separation experiments, and identify different limiting steps in the processes involving various oxygen sources and sweep gases for this 0.9 mm thick LCF-91 membrane. In the air feed-inert sweep case, the bulk diffusion and sweep side surface reaction are the two limiting steps. In the water feed-inert sweep case, surface reaction on the feed side dominates the oxygen permeation process. Yet in the water feed-fuel sweep case, surface reactions on both the feed and sweep sides are rate determining when hydrogen concentration in the sweep side is in the range of 1-5 vol%. Furthermore, long term studies show that the surface morphology changes and silica impurities have little impact on the oxygen flux for either water thermolysis or air separation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".