Limestone Acidification Using Citric Acid Coupled with Two-Step Calcination for Improving the CO<sub>2</sub> Sorbent Activity
Bibliographic record
Abstract
This work investigates the acidification of a natural limestone source using citric acid in order to produce porous calcium oxide (CaO) CO 2 sorbent, with good stability in high-temperature operation. The CO 2 sorption behavior of the proposed material was studied in several adsorption–regeneration cycles under different adsorption conditions (600, 650, and 700 °C), indicating the superior thermal stability and CO 2 adsorption capacity of the proposed material compared to untreated limestone. Acidification of natural limestone results in the production of a calcium citrate component, which easily decomposes to high-purity fibrous CaO upon calcination at 850 °C. A novel technique based on a controlled atmosphere during the calcination step (two-step treatment) was developed to improve the activity of the CaO sorbent produced from the acidified precursor. A remarkable improvement in the adsorption activity was found for samples prepared using two-step calcination (initially treated in argon, followed by calcination in air) compared to those produced by one-step calcination. The in situ carbon formed during the primary calcination in an argon atmosphere was found to control thermal sintering and promote the dispersion of large agglomerates during burning off in the secondary calcination step under an air atmosphere. The influence of the primary calcination temperature was studied in detail for the acidified sorbents prepared by either one- or two-step calcination.
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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".