Mapping the Three‐Dimensional Nanostructure of the Ionic Liquid–Solid Interface Using Atomic Force Microscopy and Molecular Dynamics Simulations
Bibliographic record
Abstract
Abstract Ionic liquids (ILs) are a widely investigated class of solvents for scientific and industrial applications due to their desirable and “tunable” properties. The IL–solid interface is a complex entity, and despite intensive investigation, its true nature remains elusive. The understanding of the IL–solid interface has evolved over the last decade from a simple 1D double layer, to a 2D ordered interface, and finally a liquid‐specific, complex 3D ordered liquid interface. However, most studies depend solely on one technique, which often only examine one aspect of the interfacial nanostructure. Here, a holistic study of the protic IL–solid interface is presented, which provides a more detailed picture of IL interfacial solvation. The 3D nanostructure of the ethylammonium nitrate (EAN)–mica interface is investigated using a combination of 1D, 2D, and 3D amplitude modulated‐atomic force microscopy and molecular dynamics simulations. Importantly, it is found that the EAN–mica interface is more complex than previously reported, possessing surface‐adsorbed, near‐surface, surface‐normal, and lateral heterogeneity, which propagates at relatively large distances from the solid substrate. The work presented in this study meaningfully enhances the understanding of the IL–solid interface.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".