Influence of Porosity on Performance of Freeze-granulated Fe2O3/Al2O3 Oxygen Carriers Used for Chemical Looping Combustion
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
Chemical looping combustion (CLC) is a promising solution for the next coal-fired power generation technology with inherent CO2 separation capability. One of the critical aspects for the development of the CLC process is to develop suitable oxygen carrier (OC) particles to transfer oxygen to the fuel in the absence of air. Relevant studies have focused on active material screening, thermodynamic analysis and operational tests. This investigation was conducted on the microstructural property of OCs, to be specific, the particle porosity effect on the performance of iron-based OCs. Fe2O3, supported on Al2O3 was used as the oxygen carrier. The effect of water content of the spray slurry used to produce the OC was varied to determine the influence of OC porosity on reactivity, oxygen transfer capacity and mechanical durability. A preliminary test was done to establish the minimum and maximum water percentage needed to make slurry. A process that included freeze granulation (FG), freeze drying, and calcination was used to prepare four samples of iron oxide/alumina with various water-to-solid phase ratios. A scanning electron microscope (SEM) was used to characterize the porosity of FG Fe2O3/Al2O3 particles. A direct relationship was observed. A Shimpo FGE-10X force gauge was used to measure the crushing strength of selected samples. A thermogravimetric analyzer (TGA) coupled with a mass spectrometer (MS) was used to study the change in reaction rates through multiple reduction-oxidation cycles of the samples. Crystallinity of the OCs in reduced and oxidized forms were confirmed by XRD analysis.
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 | 0.000 | 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) | 0.000 | 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