Thermal Activation of CaO-Based Sorbent and Self-Reactivation during CO<sub>2</sub> Capture Looping Cycles
Bibliographic record
Abstract
In this study, the thermal activation of different types of CaO-based sorbents was examined. Pretreatments were performed at different temperatures (800--1300 degrees C) and different durations (6--48 h) using four Canadian limestones. Sieved fractions of the limestones, powders obtained by grinding, and hydroxides produced following multiple carbonation/calcination cycles achieved in a tube furnace were examined. Pretreated samples were evaluated using two types of thermogravimetric reactors/ analyzers. The most important result was that thermal pretreatment could improve sorbent performance. In comparison to the original, pretreated sorbents showed better conversions over a longer series of CO2 cycles. Moreover, in some cases, sorbent activity actually increased with cycle number, and this effectwas especially pronounced for powdered samples preheated at 1000 degrees C. In these experiments, the increase of conversion with cycle number (designated as self-reactivation) after 30 cycles produced samples that were approximately 50% carbonated for the four sorbents examined here, and there appeared to be the potential for additional increase. These results were explained with the newly proposed pore--skeleton model. This model suggests, in addition to changes in the porous structure of the sorbent, that changes in the pore--skeleton produced during pretreatment strongly influence subsequent carbonation/ calcination cycles.
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.
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".