TGA and kinetic modelling of Co, Mn and Cu oxides for chemical looping gasification (CLG)
Bibliographic record
Abstract
Abstract Oxygen carriers for biomass gasification are capable of absorbing oxygen from air and desorbing it in the gasifier. Based on thermodynamic equilibrium, copper, manganese, and cobalt oxides have the highest oxygen release capacities among the different oxygen carriers. These oxygen carriers were deposited on alumina via incipient wetness impregnation. The weight loss of the CuO–Cu2O carrier, as measured in a thermo‐gravimetric analyzer, was 10 % while it was 7 % for the Co3O4–CoO couple and only 3 % for the Mn2O3–Mn3O4 couple. The optimum operating temperature for the CuO oxygen carrier was 100 °C higher compared to the other two at 950 °C. A modified nuclei growth model (MNG) characterizes the weight loss/gain during the reduction‐oxidation cycles. The reduction rate is 3 times higher at 875 °C compared to 825 °C while the oxidation rate decreases more than 10 times. The CuO carrier surface area decreased by 70 %, while it was 30 % and 60 % in the Co3O4 and Mn2O3 carriers, respectively. Cobalt has a lower tendency to sinter at high temperature compared to either copper or manganese and has a higher oxygen transport capacity and oxidation‐reduction rates. Therefore, despite its higher cost and toxicity, it might be considered as a potential oxygen carrier especially for solid fuel gasification.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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".