Molecular Insights into the Concentration-Dependent Antagonistic Effect between Asparagine and NaCl on CO<sub>2</sub> Hydrate Growth
Bibliographic record
Abstract
Gas hydrates are considered to be a promising medium for CO 2 sequestration in marine environments. Additionally, salinity and amino acids in marine environments can significantly affect the growth of hydrates. This study employs molecular dynamics (MD) simulations to investigate the influence of asparagine (Asn) on CO 2 hydrate growth within simulated seawater (NaCl 3.5 wt %) filled nanopores using analyses of cage growth rate, diffusion coefficient, radial distribution function, and electrostatic potential. Though NaCl and Asn can individually suppress hydrate growth, their special mass ratio causes the antagonistic effect to weaken their negative influence. In saline solution, the zwitterionic Asn can interact with Na + and Cl – ions to disturb their electrostatic attraction, inhibiting their crystallization and inducing their redistribution. The fewer crystals mitigate the CO 2 migration barriers toward the water phase, and the Asn–NaCl interaction alleviates their perturbations on surrounding water and CO 2 molecules to stabilize the hydrate. This proposed mechanism was validated from both frontal and lateral perspectives by increasing the NaCl concentration and varying the type of amino acid, respectively. These findings reveal the molecular mechanisms by which specific amino acids influence hydrate behavior and offer insights into optimizing CO 2 sequestration in saline environments.
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 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) | 0.001 | 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".