Sustainable System for CO2 Capturing with Multiple Products
Bibliographic record
Abstract
Currently, we are facing several serious issues, including diseases, climate change, economics, and wars. Among them, which needs to be resolved imminently to protect the future generations. Recently, we developed an innovative CO2 fixation and storage method based on the use of chemical compounds such as NaOH and CaCl2, to prevent the climate crisis. Herein, the electrolysis of a NaCl solution or seawater was performed to make the resultant NaOH react with CO2. Amines that react with CO2 were also examined. Moreover, seawater was used to produce CaCO3 rather than the CaCl2 solution. Although amines are currently used to capture CO2 from exhaust gases, the thermal treatment of the amine–CO2 complex solution is necessary to release CO2. Thermal treatment requires energy that eventually produces CO2 and induces the degradation of organic amines. However, the CO2 captured in the amine or NaOH solution was released easily by acidifying the CO2-containing solutions. Moreover, HCl could release CO2 from hydro carbonates, Ca(OH)2, Mg(OH)2, and mMgCO3·Mg(OH)2·nH2O, as well as from mineral carbonates, CaCO3 and MgCO3. This simple method of releasing the captured CO2 from the amine–CO2 complexes, hydro carbonates, and mineral carbonates is crucial to obtain pure CO2 for additional usage as an original material.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".