MétaCan
Menu
Back to cohort
Record W2328692618 · doi:10.1021/jp2059408

<sup>6</sup>Li 1D EXSY NMR Spectroscopy: A New Tool for Studying Lithium Dynamics in Paramagnetic Materials Applied to Monoclinic Li<sub>2</sub>VPO<sub>4</sub>F

2011· article· en· W2328692618 on OpenAlexaff
L. J. M. Davis, B. Ellis, T. N. Ramesh, Linda F. Nazar, A.D. Bain, Gillian R. Goward

Bibliographic record

VenueThe Journal of Physical Chemistry C · 2011
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsUniversity of WaterlooMcMaster University
Fundersnot available
KeywordsChemistryUnpaired electronIonParamagnetismRelaxation (psychology)Lithium (medication)Magic angle spinningAnalytical Chemistry (journal)Physical chemistryNuclear magnetic resonance spectroscopyMoleculeCondensed matter physicsStereochemistryPhysics

Abstract

fetched live from OpenAlex

6 Li selective inversion NMR studies are used to probe details of Li mobility in Li 2 VPO 4 F. Two crystallographically unique Li sites were resolved under magic-angle spinning (25–40 kHz) with paramagnetic shifts arising at 46 and −47 ppm (330 K). The rate of exchange between these sites was evaluated using selective inversion (or one-dimensional exchange (1D EXSY)) NMR. This methodology relies on relaxation-based experiments that provide a means for mobility time scales to be determined for materials in which Li + ions exchange slowly relative to their T 1 spin–lattice relaxation. This situation is particularly relevant to cathode materials for lithium ion batteries, where the unpaired electrons of the transition-metal centers provide a dominant mechanism for rapid relaxation. In Li 2 VPO 4 F, Li1–Li2 exchange pair jump rates extend from 24 (±1) to 55 (±4) Hz over a temperature range of 330–350 K. The activation energy for this ion exchange process was measured to be 0.44 (±0.06) eV.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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: Methods · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.018
GPT teacher head0.248
Teacher spread0.230 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

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

Citations36
Published2011
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of Physical Chemistry CSame topicAdvancements in Battery MaterialsFrench-language works237,207