Efficient Solar Thermochemical Hydrogen Production in a Reactor Train System With Thermochemical Oxygen Removal
Bibliographic record
Abstract
Abstract Solar Thermochemical Hydrogen Production (STCH) is a promising technology that uses high-temperature heat directly to split water. The authors have previously proposed a Reactor Train System (RTS) that addresses the largest source of inefficiency in state-of-the-art STCH systems — solid heat recovery — by using multiple moving reactors that exchange heat radiatively between STCH steps. In this work, another major source of inefficiency — oxygen removal during metal reduction — is addressed. Two oxygen pumping schemes are considered — vacuum pumping (VP) and thermochemical oxygen pumping (TcOP). For vacuum pumping, the modularity of RTS enables a ‘Pressure Cascade’ which reduces pumping work by a factor of four and the capex by a factor of five as compared to a single-step VP scheme. The optimized RTS + VP system achieves 31% heat-to-hydrogen conversion efficiency with ceria despite the low efficiency of vacuum pumps at low pressures. Thermochemical Oxygen Pumping (TcOP) uses a second redox material — SrFeO3 — to pump oxygen. This material is transported in reactors moving in the opposite direction to the main RTS train. The optimized RTS + TcOP achieves morethan 40% heat-to-hydrogen efficiency, while producing twice as much hydrogen per kilogram of ceria as the RTS + VP system.
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 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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".