Solar Hydrogen Production via a Samarium Oxide-Based Thermochemical Water Splitting Cycle
Bibliographic record
Abstract
The computational thermodynamic analysis of a samarium oxide-based two-step solar thermochemical water splitting cycle is reported. The analysis is performed using HSC chemistry software and databases. The first (solar-based) step drives the thermal reduction of Sm2O3 into Sm and O2. The second (non-solar) step corresponds to the production of H2 via a water splitting reaction and the oxidation of Sm to Sm2O3. The equilibrium thermodynamic compositions related to the thermal reduction and water splitting steps are determined. The effect of oxygen partial pressure in the inert flushing gas on the thermal reduction temperature (TH) is examined. An analysis based on the second law of thermodynamics is performed to determine the cycle efficiency (ηcycle) and solar-to-fuel energy conversion efficiency (ηsolar−to−fuel) attainable with and without heat recuperation. The results indicate that ηcycle and ηsolar−to−fuel both increase with decreasing TH, due to the reduction in oxygen partial pressure in the inert flushing gas. Furthermore, the recuperation of heat for the operation of the cycle significantly improves the solar reactor efficiency. For instance, in the case where TH = 2280 K, ηcycle = 24.4% and ηsolar−to−fuel = 29.5% (without heat recuperation), while ηcycle = 31.3% and ηsolar−to−fuel = 37.8% (with 40% heat recuperation).
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".