Sorption enhanced steam methane reforming by <scp>Ni</scp>/<scp>CaO</scp>/mayenite combined systems: Overview of experimental results from <scp>E</scp>uropean research project <scp>ASCENT</scp>
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Work Package 4 (WP4) of the European research project ASCENT developed combined sorbent‐catalyst materials (CSCM) for sorption enhanced steam methane reforming (SESMR), based on nickel (Ni) and calcium oxide (CaO). This work summarizes the whole experimental study carried out in ASCENT WP4 on Ni/CaO/mayenite systems obtained from wet mixing and wet impregnation synthesis methods. Effects from Ni precursor (Ni (CH 3 COO) 2 · 4H 2 O or Ni(NO 3 ) 2 · 6H 2 O), Ni load (from 3 wt%‐10 wt%), and free CaO load (from 0 wt%‐54 wt%) were investigated for 26 materials by means of characterizations and reforming reactivity tests in a packed‐bed microreactor (650°C, 1 atm). Thanks to comparative analyses of the results, evidence emerged about the detrimental influence of low Ni/CaO ratio on the reforming catalytic activity of solid inventories, made of CSCM or even of the raw mixing of CaO‐mayenite and Ni‐mayenite particles. Catalytic materials were active towards reforming only when derived from Ni(NO 3 ) 2 · 6H 2 O. Based on this, the best CSCM (with the lowest free CaO content and the highest Ni load from nickel nitrate) was chosen to further study its industrial applicability by multicycle SESMR/sorbent‐regeneration tests in a bench‐scale packed‐bed rig and attrition tests according to ASTM D5757‐11. The CSCM was stable and active for 200 cycles with regenerations in N 2 at 850°C, while a progressive loss of its activity occurred with regenerations in CO 2 at 925°C as the cycle number increased due to Ni sintering. Its performance in the attrition tests was comparable to that of calcined dolomite.
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.
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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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 it