High Ion‐Conducting Solid‐State Composite Electrolytes with Carbon Quantum Dot Nanofillers
Bibliographic record
Abstract
Abstract Solid‐state polymer electrolytes (SPEs) with high ionic conductivity are desirable for next generation lithium‐ and sodium‐ion batteries with enhanced safety and energy density. Nanoscale fillers such as alumina, silica, and titania nanoparticles are known to improve the ionic conduction of SPEs and the conductivity enhancement is more favorable for nanofillers with a smaller size. However, aggregation of nanoscale fillers in SPEs limits particle size reduction and, in turn, hinders ionic conductivity improvement. Here, a novel poly(ethylene oxide) (PEO)‐based nanocomposite polymer electrolyte (NPE) is exploited with carbon quantum dots (CQDs) that are enriched with oxygen‐containing functional groups. Well‐dispersed, 2.0–3.0 nm diameter CQDs offer numerous Lewis acid sites that effectively increase the dissociation degree of lithium and sodium salts, adsorption of anions, and the amorphicity of the PEO matrix. Thus, the PEO/CQDs‐Li electrolyte exhibits an exceptionally high ionic conductivity of 1.39 × 10 −4 S cm −1 and a high lithium transference number of 0.48. In addition, the PEO/CQDs‐Na electrolyte has ionic conductivity and sodium ion transference number values of 7.17 × 10 −5 S cm −1 and 0.42, respectively. It is further showed that all solid‐state lithium/sodium rechargeable batteries assembled with PEO/CQDs NPEs display excellent rate performance and cycling stability.
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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 | 0.001 |
| 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".