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Record W4245820583 · doi:10.1149/ma2019-02/40/1838

(Invited) Structure of NMC Family Cathodes from Monte Carlo Simulation and NMR Spectroscopy

2019· article· en· W4245820583 on OpenAlexaff
Kristopher J. Harris, Chelsey Hurst, Gillian R. Goward, Jamie M. Foster

Bibliographic record

VenueECS Meeting Abstracts · 2019
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNMR spectroscopy and applications
Canadian institutionsMcMaster University
Fundersnot available
KeywordsUnpaired electronMonte Carlo methodValence (chemistry)SpectroscopyIonMaterials scienceElectronCathodeChemistryCrystallographyChemical physicsMoleculePhysicsPhysical chemistry

Abstract

fetched live from OpenAlex

The cathode active material Li[Ni1/3Mn1/3Co1/3O2], NMC 111, is well known for its excellent cycling lifetime and capacity. It is also well known that varying the ratios of transition metals (TMs), Li atoms, and vacancies1 can provide capacity increases of up to 40 %, though at the expense of structural stability. Not only are these structural-breakdown pathways not understood, full models of even the pristine materials are not available because of the significant disorder. It seems apparent that better structural characterization tools are needed to understand these materials, and therefore may finally provide the key to unlocking their potential. Here, we present a Monte-Carlo structure-solution method, using a Hamiltonian that is based on local electroneutrality [2]. The close-packed 2D TM sheet of the NMC family is partitioned according to valence-bonding principles and a state of local charge balance—for the TM atoms with respect to the neighboring (fixed) 2D oxygen sheet—is sought. This simple Hamiltonian allows rapid, yet realistic, sampling of the configuration space of the large sheets (up to 10,000 TM atoms) necessary to properly capture the (often) complex arrangements. The predicted structural models are verified as accurate using 7Li NMR spectroscopy. The unpaired electrons of the TM atoms generate large paramagnetic chemical shifts in the neighboring Li atoms [3]. The 7Li spectra are therefore sensitive to the identity of the 12 TM atoms neighboring each Li: 6 in the TM sheet above, 6 in the sheet below [3]. Notably, the orientation dependence of this effect is not a hindrance when using 7Li MATPASS NMR spectroscopy under 60 kHz MAS [4]. A series of samples with compositions Li[Ni x Mn x Co1-2x O2] are investigated, where x = 2%, 10%, and 33%. In each case, structures generated by the Monte Carlo calculations are verified through an extremely accurate matching between predicted and experimental 7Li NMR spectra. Additionally, accurate 3D simulations of electrochemically charged versions of these samples, where transition-metal oxidation and delithiation occur, are presented. Reference s : [1] R. Shunmugasundaram, S. Arumugam, J.R. Dahn Chem. Mater. 25 (2015) 989. [2] K.J. Harris et al. Chem. Mater. 29 (2017) 5550. [3] D. Zeng et al. Chem. Mater. 19 (2007) 6277. [4] I. Hung et al. J. Am. Chem. Soc. 134 (2012) 1898. Figure 1

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.048

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0140.001

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 head0.010
GPT teacher head0.293
Teacher spread0.284 · 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 designSimulation or modeling
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
Published2019
Admission routes1
Has abstractyes

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