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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".