Adsorption of Cations Using Graphene Oxide Loaded with Ionic Liquid: A Molecular Dynamic Simulation
Bibliographic record
Abstract
Sea water desalination is the process of separating salts from water to produce potable water.It is a very important process for countries that lack fresh water sources such as UAE.Many techniques are used in this process, but the most common one is the reverse osmosis (RO) process, which uses semipermeable membranes.Although this technique is widely used, it has a major drawback which is membrane fouling.Membrane fouling is caused due to inorganic salt deposition.This problem decreases the efficiency of the process and increases the costs.To solve the problem of inorganic salts fouling on the water filtration/desalination membranes, a proposed solution is to treat saltwater using a new adsorbent called GO-IL before sending it to the desalination process.GO-IL is an adsorbent made of graphene oxide nanosheets loaded with an ionic liquid called propylammonium nitrate.Treating saltwater using GO-IL aims to adsorb significant amounts of salts (e.g.Na + and Mg 2+ ) present in water before starting the desalination process.This pretreatment process reduces desalination membrane fouling, enhances the desalination process, and reduces cleaning and maintenance costs.The purpose of our work is to simulate the water pretreatment process using GO-IL on a molecular level scale using the computational power of Molecular Dynamics (MD) Simulations to test its efficiency.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".