<i>β</i>-NMR measurements of molecular-scale lithium-ion dynamics in poly(ethylene oxide)-lithium-salt thin films
Bibliographic record
Abstract
β -detected NMR (β-NMR) has been used to study the molecular-scale dynamics of lithium ions in thin films of poly(ethylene oxide) (PEO) containing either lithium bis(trifluoromethanesulfonyl)imide (LiTFSI) or lithium trifluoroacetate (LiTFA) salts at monomer-to-salt ratios (EO/Li) of 8.3. The results are compared with previous β-NMR measurements on pure PEO and PEO with lithium triflate (LiOTf) at the same loading [McKenzie et al., J. Am. Chem. Soc. 136, 7833 (2014)]. Activated hopping of 8Li+ was observed in all of the films above ∼250 K, with the hopping parameters strongly correlated with the ionicity of the lithium salt rather than the polymer glass transition temperature. The pre-exponential factor increases exponentially with ionicity, while the activation energy for hopping increases approximately linearly, going from 6.3±0.2 kJ mol−1 in PEO:LiTFA to 17.8±0.2 kJ mol−1 in PEO:LiTFSI. The more rapid increase in the pre-exponential factor outweighs the effect of the larger activation energy and results in 8Li+ hopping being fastest in PEO followed by PEO:LiTFSI, PEO:LiOTf, and PEO:LiTFA.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".