Clay Minerals—Ionic Liquids, Nanoarchitectures, and Applications
Bibliographic record
Abstract
Abstract Clay minerals, whose world resources are extremely large, have the potential to be more exploited as the basis for functional materials. Of interest are their interactions with ionic liquids (ILs). These compounds have found a large number of applications in the last few decades due to unique properties, such as low vapor pressure, high thermal stability, and remarkable solvation abilities. In the case of the swelling smectites, the organic cation of ILs can replace interlayer cations and find applications in the preparation of nanocomposites. This feature article is mainly focused on kaolinite, a nonswelling 1:1 phyllosilicate, whose layers are essentially neutral. Consequently, the intercalation of ILs involves both cation and anion. The organic cations can be designed to bear hydroxyl groups that will react with the aluminol internal surface of kaolinite, resulting in ionic liquids not only intercalated but also grafted. The resulting nanohybrid materials are characterized by a fixed, rigid, constrained 2D structure, whose dimension can be tuned by the size of the organic cation, whereas the anion is exchangeable. These materials are used for sensing applications such as the specific detection of anions as well as their quantitative analysis. They are also used as catalyst support for nanoparticles.
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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 | 0.000 |
| 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".