Amino Acid-Driven One-Step Process to Transform CO<sub>2</sub> into Barium Carbonate Nanoparticles
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Controlling carbon dioxide (CO 2 ) emission is critical since CO 2 is the primary contributor to global warming, posing significant threats to human survival and leading to unwanted changes in biological systems. Carbon Capture, Utilization, and Storage (CCUS) has been studied worldwide, but methods that are effective, efficient, economical, and environmentally friendly are lacking. In this study, we developed a unique method to achieve CO 2 capture and conversion in a single step. The process of capturing and converting CO 2 into value-added BaCO 3 nanoparticles (nano-BaCO 3 ) is detailed as an illustrative example. Nano-BaCO 3, with an average size of 85 nm, was obtained without requiring external energy. The use of amino acid resulted in faster CO 2 absorption and more uniform particle sizes compared to the same process without the use of amino acid. Moreover, the amino acid was recycled and reused without any additional treatment, and nano-BaCO 3 production was observed in multiple cycles. In addition, our method has been proven to be applicable for CO 2 conversion from simulated flue gases with various CO 2 levels (e.g., 4% and 10%).
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".