A data‐driven optimization approach for the molecular design of <scp> CO <sub>2</sub> </scp> capture ionic liquids mixtures
Bibliographic record
Abstract
Abstract CO emissions into the atmosphere have become a global concern in recent years. The amount of CO generated in post‐combustion processes has attracted the attention of the international scientific community. Some processes use alkanolamines to absorb CO; however, their volatility increases the process cost. An alternative to this limitation is the use of ionic liquids (ILs) as solvents in the CO absorption process. An important characteristic of ILs is their extremely low vapour pressure, making them practically non‐volatile. In this work, molecular dynamics simulation (MDS) was employed to calculate the absorption capacity of an IL mixture, and a design of experiments approach guided by Bayesian optimization (BO) was used to find an optimal IL mixture that maximizes the amount of CO captured. The applied methodology helps to reduce the number of numerical experiments and, consequently, computation time. The IL mixture analyzed was [BMIM][NTF] and [EMIM][SCN] (1‐ethyl‐3‐methylimidazolium bis(trifluoromethylsulfonyl)imide and 1‐ethyl‐3‐methylimidazolium thiocyanate) due to their known effectiveness as absorbents. With only 12 simulations, the composition of the IL mixture that achieve maximum CO absorption was determined. The results obtained encourage further exploration of other IL mixtures that may absorb greater amounts of CO.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".