A Quantitative Metric for the Design of Selective Supercritical CO<sub>2</sub> Extraction of Lithium from Geothermal Brine
Bibliographic record
Abstract
Abstract As demand grows for lithium, its recovery from geothermal brines provides an attractive alternative to slow mining. One promising extraction method uses crown ethers as extractants in supercritical carbon dioxide with cation exchangers to facilitate extraction from brine. Molecular dynamics modeling is used to understand the mechanism of binding between lithium (or sodium) and combinations of 14‐crown‐4 ethers and cation exchangers, and the predictive capability of computational modeling to test lithium selectivity is established for four combinations of crown ethers [methylene‐14‐crown‐4 (M14C4) and a fluorinated 14‐crown‐4 (F14C4)] and cation exchangers [di(2‐ethyl‐hexyl)phosphoric acid (HDEHP) and tetraethylammonium perfluoro‐1‐octanesulfonate (TPFOS)]. Binding free energies (given in kcal mol−1) of lithium and sodium, respectively, to crown ether–cation exchangers are 85 and 71 for M14C4–HDEHP, 90 and 71 for F14C4–HDEHP, 93 and 80 for M14C4–TPFOS, and 104 and 93 for F14C4–TPFOS. Good agreement is found between computational predictions and supercritical carbon dioxide extraction experiments at 60 °C and 250 bar. Binding free energy gives a suitable metric to describe extraction efficiency. Differences in the binding free energies of sodium and lithium to crown ethers determine the extraction selectivity. Fluorine groups are found to exert a positive influence to optimize extraction efficiency. Of the systems studied, F14C4 with TPFOS offers the most selective and efficient extraction system.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".