CeO2-ZrO2 nanocomposite-stabilized Ni/CaO bifunctional material: Novel candidate for sustainable hydrogen production via SESR of bio-oil
Bibliographic record
Abstract
A well-designated CeO 2 -ZrO 2 nanocomposite (CZ) was successfully synthesized using a hydrothermal approach. For the first time, CZ was examined as i) supporting material for Ni catalyst (Ni-CZ) during steam reforming of bio-oil (SRBO) and ii) stabilizing material for Ni/CaO catalyst/sorbent bifunctional material (Ni/(CaO-xCZ)) during the intensified sorption-enhanced SRBO (SESRBO) process. The impact of CZ wt.% (x: ranging from 0% to 25%) and mixing pattern (mechanical mixture and bifunctional material (BFM)) on sorption capacity and stability were evaluated over 15 carbonation/calcination cycles. All CZ-stabilized CaO (CaO-xCZ) materials, especially CaO-20CZ, demonstrated high cyclic stability due to the generation of CaZrO 3 perovskite structure that serves as a barrier against CaO sintering. Compared to mechanical mixing pattern (Ni-CZ (catalyst) + CaO (sorbent)), Ni/(CaO-20CZ) displayed 27% higher CaO conversion at 15 th carbonation cycle. At 600°C and steam-to-carbon (S/C) ratio of 3, bio-oil conversion of 96.2% and H 2 yield of 75.8% were achieved over Ni-CZ catalyst during SRBO reaction. Furthermore, Ni/(CaO-20CZ) BFM led to a hydrogen purity of approximately 94% during the pre-breakthrough period (maximum intensified period), sustained for 33 min. Ni/(CaO-20CZ) also maintained a high cyclic stability without noticeable decline in terms of H 2 purity and yield over 10 consecutive SESRBO cycles.
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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) | 0.000 | 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) | 0.001 | 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".