Ionic Conductivity of Nanostructured Hybrid Materials Designed from Imidazolium Ionic Liquids and Kaolinite
Bibliographic record
Abstract
Highly nanostructured hybrid materials were prepared by the intercalation into the interlayer spaces of kaolinite of ionic liquids based on imidazolium derivatives. Their structure, thermal behavior, and composition were characterized by a range of methods, including X-ray diffraction, solid-state NMR, thermal gravimetric analysis, and elemental analysis. Measurements of their electrical conductivity were carried out by impedance spectroscopy in the temperature range 23−250 °C. Three imidazolium derivatives were used: 1-methyl 3-propyl imidazolium bromide (Im-1), 1-methyl 3-(2-chloroethyl) imidazolium cloride (Im-2), and 1-methyl 3-(benzyl) imidazolium chloride (Im-3). The electrical conductivity depends on the size as well as on the structural organization of the salts into the interlayer space. In the case of the intercalates Im-1-K and Im-2-K, the electrical conductivity measured at room temperature is about 2 × 10 −5 S cm −1 . This value varies with temperature. The maximum of conductivity, 4 × 10 −4 S cm −1, was obtained in a relatively short-range of temperatures, between 160 and 200 °C. No conductivity could be measured in the case of Im-3-K. An optimization of the structures of the three nanohybrid materials was performed using the PM6 semiempirical method. Alternating channels of organic cations and halide anions are formed in the cases of Im-1-K and Im-2-K, leading to the observed ionic conductivity behavior. In the presence of a bulkier substituent of the imidazolium ring, such as in Im-3-K, the anionic channels are blocked by the substituent, resulting in the absence of conductivity. To the best of our knowledge, this is the first report of electrical conductivity displayed by an interlayer modification of kaolinite.
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) | 0.000 | 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".