Capturing and converting CO2 using amino acids as various commercially valuable nano-carbonates
Bibliographic record
Abstract
Carbon dioxide (CO 2 ) is the most significant greenhouse gas and one of the strategies to reduce CO 2 emission is to convert CO 2 into commercially valuable products. Currently, methods that can effectively capture and convert CO 2 into carbonate nanomaterials, which have unique applications in various fields, have rarely been reported, and there are no universal methods that can capture and convert CO 2 into different nano-carbonates. In this study, an innovative two-step strategy based on amino acids was developed to produce multiple different carbonate nanoparticles , including CaCO 3 , BaCO 3 , and Ag 2 CO 3 nanoparticles with diameters of 70 nm, 50 nm, and 7 nm, respectively. Fundamentally important, the nuclear magnetic resonance data clearly demonstrated that it was the amino acids (e.g., glycine) that dictated the formation of carbonate nanoparticles. In the presence of amino acids, a competition in forming nanoparticles and microparticles was observed, and the formation of nanoparticles was proportional to the carbamate formed from CO 2 reacting with amino acids. In the absence of amino acids, carbonate microparticles (∼ 2 µm) were formed.
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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".