Innovative use of CaO in combination with amino acid salt to convert CO2 as CaCO3 nanoparticles under mild pH and low temperature
Bibliographic record
Abstract
The increasing demand for sustainable CO 2 management has driven the development of innovative methods that can convert point source CO 2 into value-added products. In this study, CaO in combination with amino acid salt was used to convert CO 2 into CaCO 3 nanoparticles. Different from the conventional method where CO 2 diffusion presents a major challenge in reacting with CaO to form CaCO 3 , amino acid salt solvent was applied to absorb CO 2 first and then rapidly reacted with CaO to form CaCO 3 nanoparticles (∼50 nm) at a low temperature (e.g., 60 °C). Our experiments showed that at a glycine (Gly)/NaOH ratio of 2:1 or 3:1, the solution pH values during the CO 2 absorption and conversion were about 8–9 at 60 °C, while at a ratio of 1:1, the solution pH values were about 9–11; without Gly, the solution pH values were about 12. Gly-optimized solvent substantially reduced corrosion risk to reactors. In addition, the use of amino acid (i.e., Gly) led to much smaller CaCO 3 particles, distinctly different chemical phases, and fundamentally different chemical reactions. Moreover, in the presence of Gly, the solution pH was completely reversed and the solution was regenerated for cyclic use when CaO was added. The solvent was recyclable and reusable, highlighting the cost-effectiveness and sustainability of this approach. The Gly-modulated CaCO 3 nanoparticles may have significant potential for industrial applications in the biomedicine, construction, plastics, and rubber industries. • Solvent pH was relatively low and maintained between pH 8–10. • CO 2 was mineralized into CaCO 3 nanoparticles at 60 °C with particle size of ∼50 nm. • Solvent was regenerated and reused without additional treatment. • Size and shape of CaCO 3 nanoparticles remained consistent when solvent was reused.
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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) | 0.000 | 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 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".