Nanocomposite Electrolytes with Fumed Silica and Hectorite Clay Networks: Passive versus Active Fillers
Bibliographic record
Abstract
Abstract The use of nanocomposites constitutes a versatile and robust approach in the development of novel electrolytes with tailored electrochemical and mechanical characteristics. In this study, we examine the morphology, rheology, and ion‐transport properties of two types of nanocomposite electrolyte gels, one consisting of branched silica nanoparticles and the other composed of hectorite clay. In the first system with hydrophobic (fumed) silica, oligomers of poly(ethylene oxide) (PEO), and lithium salt, the silica acts as a passive filler and does not participate in ion transport. The electrochemical properties are controlled by the salt–PEO electrolyte, allowing for ionic conductivities greater than 10–3 S cm–1 at ambient temperature. At sufficiently high concentrations, the silica forms an elastic gel possessing a large open network structure that provides for unimpeded ion mobility. In the second system composed of lithium‐exchanged hectorite filler, the nanoscale platelets serve as the anion. This active filler yields ionic conductivities in excess of 10–4 S cm–1 and lithium transference numbers approaching unity. Similar to fumed silica, the hectorite clay also forms an elastic gel network. However, the morphologies of the two systems are distinctively different both in terms of network structure and characteristic length scale. These morphological differences manifest themselves in different rheological responses with regard to gel modulus and yield stress.
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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) | 0.000 | 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".