ZrO<sub>2</sub>–CuO Sorbents for High-Temperature Air Separation
Bibliographic record
Abstract
The citrate gel method was used to synthesize ZrO 2 samples loaded with different concentrations of CuO for potential use as oxygen sorbents for high-temperature air separation. The effect of yttria addition was investigated by studying the change in the crystal structure (X-ray diffraction) and morphology (scanning electron microscopy). Oxygen sorption and desorption kinetics of all samples and their cyclic stability were tested using thermogravimetric analysis. In oxygen desorption studies, a mixture of CO 2 and O 2 similar to recycled flue gas in composition was used as purges gas to study the possibility of using these sorbents in the ceramic autothermal recovery process. Yttria addition improved the cyclical stability of the sorbents without any significant change in sorptive/desorptive properties. Sorbents with CuO loading below 20% showed improved performance with a sorption capacity higher than existing perovskite or spinel/perovskite oxides. Yttria doping improves dispersion of CuO on the support therefore leading to higher desorption rates. Yttria doping also prevents the agglomeration of Cu 2 O particles in desorption cycles making the rate of desorption and absorption stable over long time periods.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".