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Record W2344887296 · doi:10.1149/ma2014-02/5/469

Multinuclear Solid-State NMR Studies of Cathode Materials for Lithium and Sodium Ion Batteries

2014· article· en· W2344887296 on OpenAlexaff
Danielle L. Smiley, Matteo Z. Tessaro, Gillian R. Goward

Bibliographic record

VenueECS Meeting Abstracts · 2014
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsMcMaster University
Fundersnot available
KeywordsLithium (medication)CathodeElectrochemistryMaterials scienceBattery (electricity)IonOxideGravimetric analysisChemical engineeringNanotechnologyInorganic chemistryChemistryElectrodePhysical chemistryMetallurgyOrganic chemistryThermodynamics

Abstract

fetched live from OpenAlex

Lithium ion batteries have long been promised as an alternative energy technology, owing to their high volumetric and gravimetric energy densities.1 The cathode (positive electrode) material is of particular importance as the choice of this component can dictate overall battery performance. Polyanionic structural frameworks such as phosphates or fluorosulfates are an attractive alternative to the traditional layered oxide cathode structures due to their high electrochemical and thermal stability.2 More recently, the reconsideration of sodium ion batteries has become popular, and while the larger, heavier, sodium ion decreases overall energy density for a given material vs. Li, the cost and abundance benefits are expected to largely outweigh this disadvantage.3 Despite considerable research efforts to improve cathode materials for both lithium and sodium ion batteries, the role of structure on ion mobility is not yet fully understood; a property integral to electrochemical success. Solid-state nuclear magnetic resonance (ssNMR) is a valuable tool for studying these polyanionic structures, as it is sensitive to both the mobile ion of interest (23Na or 6Li/7Li) as well as the surrounding structural framework (31P, 19F). The fluorophosphate family of sodium cathode materials is extremely promising, as the materials are both thermally and electrochemically stable, with the advantage of being relatively inexpensive depending on the redox active transition metal of choice. In particular, the electrochemical performance of Na2MPO4F (M= Fe, Co, Ni, Mn) has been investigated in recent years, and is found to be an exemplary candidate for use in Na ion batteries.4 We have utilized 23Na NMR to gain understanding of the structural changes that occur during electrochemical extraction and reinsertion of Na ions during the charging and discharging processes of a number of fluorophosphate analogues. The paramagnetic nature of many of these materials makes their investigation by NMR non-trivial, requiring the use of fast magic-angle spinning (MAS) and low external magnetic fields. In particular, the structural changes occurring upon desodiation of Na2FePO4F were investigated by NMR, where the attenuation of one of the two sites is attributed to the selective removal of Na ions from a single crystallographic position. Additionally, by cycling Na2FePO4F versus a Li-metal counter electrode we observe evidence of substantial Na-Li ion exchange by 7Li NMR, opening the door for interesting heteronuclear correlation experiments with a hybrid Na/Li cathode material. The movement of Li ions as a function of electrochemical cycling can also be tracked in Li cathode materials. Receiving significant attention is the fluorosulfate structure of the form LiFeSO4F, which benefits from a high redox potential and excellent electrochemical behaviour.5,6 6,7Li ssNMR was used here to identify any changes to the Li environment at various stages along the electrochemical charge/discharge cycle, providing valuable insight into the structural stability of this material. (1) Tarascon, J.-M.; Armand, M. Nature 2001. (2) Padhi, A. K.; Nanjundaswamy, K. S.; Masquelier, C.; Masque; Okada, S.; Goodenough, J. B. J. Electrochem. Soc. 1997, 144, 1609–1613. (3) Ellis, B. L.; Nazar, L. F. Current Opinion in Solid State & Materials Science 2012, 16, 168–177. (4) Ellis, B. L.; Makahnouk, W. R. M.; Rowan-Weetaluktuk, W. N.; Ryan, D. H.; Nazar, L. F. Chem. Mater. 2010, 22, 1059–1070. (5) Barpanda, P.; Ati, M.; Melot, B. C.; Rousse, G.; Chotard, J.-N.; Doublet, M.-L.; Sougrati, M. T.; Corr, S. A.; Jumas, J.-C.; Tarascon, J.-M. Nature Materials 2011, 10, 772–779. (6) Tripathi, R.; Gardiner, G. R.; Islam, M. S.; Nazar, L. F. Chem. Mater. 2011, 23, 2278–2284. Figure 1: 23Na 30 kHz MAS NMR spectra of pristine Na2FePO4F (red) and electrochemically generated Na1.5FePO4F (blue) demonstrating the decrease in intensity at site A during cycling as well as the formation of a novel site (#).

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)0.001
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.024
GPT teacher head0.283
Teacher spread0.259 · 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
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".

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Citations0
Published2014
Admission routes1
Has abstractyes

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