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Record W4391639907 · doi:10.1149/ma2023-022184mtgabs

Quantitative Determination of the Compositional Inhomogeneity in NMC Cathode Materials by Williamson-Hall Analysis

2023· article· en· W4391639907 on OpenAlexaff
Mohammad H. Tahmasebi, M. N. Obrovac

Bibliographic record

VenueECS Meeting Abstracts · 2023
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsDalhousie University
Fundersnot available
KeywordsHall effectCathodeMaterials sciencePhysicsAnalytical Chemistry (journal)ChemistryChromatographyPhysical chemistryMagnetic fieldQuantum mechanics

Abstract

fetched live from OpenAlex

Over the past decades, the co-precipitation synthesis technique has been used, particularly for making LiNixMnyCozO2 (NMC) cathode materials for lithium-ion batteries (LIBs), due to better control of particle morphology and higher compositional homogeneity in the final NMC product compared to other methods. However, the co-precipitation method introduces complexity and consumes large amounts of energy and water, which can increase cost. This has led researchers to investigate solid-state NMC synthesis techniques in order to improve sustainability and reduce LIB cost [1-3]. Obtaining homogeneous mixing of transition metals at the atomic scale by solid-state methods remains a challenge. Since compositional homogeneity has a vital effect on the electrochemical performance of the NMCs, it is essential to quantitatively estimate the degree of inhomogeneity in cathode materials for the development of new synthesis methods. In the present study, a quantitative X-ray diffraction (XRD) analysis technique using the Williamson-Hall (WH) method is used to determine the degree of inhomogeneity in NMC precursors and NMC cathode materials. In this regard, single-phase rock salt (RS) precursors with varying degrees of transition metal homogeneity were prepared by combining NiO, MnO and CoO by grinding for different times, followed by heating under an Ar flow. Then, NMC cathode materials were made by heating the RS-precursors with lithium carbonate in air. EDS elemental maps showed that transition metal inhomogeneity existed in the RS-precursors and final NMCs and that this inhomogeneity is primarily associated with the Mn distribution (Figure 1(a)). Furthermore, the compositional inhomogeneity became reduced with increasing precursor grinding time. Using a WH analysis method, composition variation in the NMC precursor and in the final NMC product could be quantitatively determined from conventional XRD powder patterns. Utilizing this analysis, it was found that inhomogeneity in the precursor is translated into the final NMC, so that a similar trend in the degree of inhomogeneity was observed for both synthesized precursors and their NMC products (Figure 1(b) and (c)). Additionally, NMCs with high compositional homogeneity showed much better electrochemical performance compared to the samples with higher degree of inhomogeneity. We believe the proposed method is highly useful in the development of new cathode precursors and in predicting the performance of the NMC cathode materials by quantitatively determining their degree of compositional inhomogeneity. References 1. M. N. Obrovac, L. Zheng, and M. D. L. Garayt, Engineered Particle Synthesis by Dry Particle Microgranulation, Cell Reports Physical Science, 1, 100063 (2020). 2. L. Zheng, J. C. Bennett, and M. N. Obrovac, All-Dry Synthesis of Single Crystal NMC Cathode Materials for Li-Ion Batteries, J. Electrochem. Soc., 167, 130536 (2020). 3. M. H. Tahmasebi, L. Zheng, T. D. Hatchard, M. N. Obrovac, Li[Ni0.6Mn0.2Co0.2]O2 Made From Crystalline Rock Salt Oxide Precursors, J. Electrochem. Soc., 170, 030531 (2023). 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)0.001
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 score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
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 head0.020
GPT teacher head0.279
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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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