Achieving enhanced ionic mobility in nanoporous silica by controlled surface interactions
Bibliographic record
Abstract
ImTFSI), and one protic ammonium based (diethylmethylammonium methanesulfonate, DEMAOMs) ionic liquid. Both these ionic liquids display higher ionic mobility when confined in functionalized silica as compared to untreated silica nano-pores, an improvement that is more pronounced at low pore filling factors (i.e. in the nano-sized pore domains) and observed in the whole temperature window investigated (i.e. from -10 to 140 °C). Solid-state NMR, diffusion NMR and dielectric spectroscopy concomitantly demonstrate this effect. The origin of this enhancement is explained in terms of weaker intermolecular interactions and a consequent flipped-ion effect at the silica interface strongly supported by 2D solid-state NMR experiments. The possibility to significantly enhance the ionic mobility by controlling the nature of surface interactions is extremely important in the field of materials science and highlights these structurally tunable gels as promising solid-like electrolytes for use in energy relevant devices. These include, but are not limited to, Li-ion batteries and proton exchange membrane (PEM) fuel cells.
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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".