Coupling Particle Ordering and Spherulitic Growth for Long-Term Performance of Nanocellulose/Poly(ethylene oxide) Electrolytes
Bibliographic record
Abstract
Development of lithium-ion batteries with composite solid polymer electrolytes (CPSEs) has attracted attention due to their higher energy density and improved safety compared to systems utilizing liquid electrolytes. While it is well known that the microstructure of CPSEs affects the ionic conductivity, thermal stability, and mechanical integrity/long-term stability, the bridge between the microscopic and macroscopic scales is still unclear. Herein, we present a systematic investigation of the distribution of TEMPO-oxidized cellulose nanofibrils (t-CNFs) in two different molecular weights of poly(ethylene oxide) (PEO) and its effect on Li + ion mobility, bulk conductivity, and long-term stability. For the first time, we link local Li-ion mobility at the nanoscale level to the morphology of CPSEs defined by PEO spherulitic growth in the presence of t-CNF. In a low-MW PEO system, spherulites occupy a whole volume of the derived CPSE with t-CNF being incorporated in between lamellas, while their nuclei remain particle-free. In a high-MW PEO system, spherulites are scarce and their growth is arrested in a non-equilibrium cubic shape due to the strong t-CNF network surrounding them. Electrochemical strain microscopy and solid-state 7 Li nuclear magnetic resonance spectroscopy confirm that t-CNF does not partake in Li + ion transport regardless of its distribution within the polymer matrix. Free-standing CSPE films with low-MW PEO have higher conductivity but lack long-term stability due to the existence of uniformly distributed, particle-free, spherulite nuclei, which have very little resistance to Li dendrite growth. On the other hand, high-MW PEO has lower conductivity but demonstrates a highly stable Li cycling response for more than 1000 h at 0.2 mA/cm 2 and 65 °C and more than 100 h at 85 °C. The study provides a direct link between the microscopic dynamic, Li-ion transport, bulk mechanical properties and long-term stability of the derived CPSE and, and as such, offers a pathway towards design of robust all-solid-state Li-metal batteries.
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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".