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Record W2248278631 · doi:10.1021/ja503066a

β-NMR Measurements of Lithium Ion Transport in Thin Films of Pure and Lithium-Salt-Doped Poly(ethylene oxide)

2014· article· en· W2248278631 on OpenAlexaff
Iain McKenzie, Masashi Harada, R. F. Kiefl, C. D. P. Levy, W. A. MacFarlane, G. D. Morris, Shinichi Ogata, M. R. Pearson, Jun Sugiyama

Bibliographic record

VenueJournal of the American Chemical Society · 2014
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Materials and Technologies
Canadian institutionsUniversity of British ColumbiaSimon Fraser UniversityTRIUMF
Fundersnot available
KeywordsChemistryLithium (medication)Relaxation (psychology)Ethylene oxideArrhenius equationAtmospheric temperature rangeIonDiffusionConductivityDopingDielectricIonic conductivityOxideGlass transitionAnalytical Chemistry (journal)Salt (chemistry)PolymerPhysical chemistryActivation energyThermodynamicsMaterials scienceOrganic chemistryElectrode

Abstract

fetched live from OpenAlex

β-Detected nuclear spin relaxation of (8)Li(+) has been used to study the microscopic diffusion of lithium ions in thin films of poly(ethylene oxide) (PEO), where the implanted lithium ions are present in extremely low concentration, and PEO with 30 wt % LiCF3SO3 over a wide range of temperatures both above and below the glass transition temperature. Recent measurements by Do et al. [Phys. Rev. Lett. 2013, 111, 018301] found that the temperature dependence of the Li(+) conductivity was identical to that of the dielectric α relaxation and was well described by the Vogel-Fulcher-Tammann relation, implying the α relaxation dominates the Li(+) transport process. In contrast, we find the hopping of Li(+) in both samples in the high temperature viscoelastic phase follows an Arrhenius law and depends significantly on the salt content. We propose that the hopping of Li(+) between cages involves motion of the polymer but that it is only for long-range diffusion where the α relaxation plays an important role.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.346

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.012
GPT teacher head0.225
Teacher spread0.213 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations27
Published2014
Admission routes1
Has abstractyes

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