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Record W4416603230 · doi:10.1149/ma2025-025779mtgabs

<i>(Invited)</i> Advanced Alloy Anodes for High Energy Density Sodium-Ion Cells

2025· article· W4416603230 on OpenAlexaff
Martins Obialor, Matthew D. L. Garayt, Libin Zhang, Ian Monchesky, Meredith Tulloch, Svena Yu, J. R. Dahn, Michael Metzger

Bibliographic record

VenueECS Meeting Abstracts · 2025
Typearticle
Language
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsDalhousie University
Fundersnot available
KeywordsAnodeElectrodeAlloyCarbon fibersVolume (thermodynamics)TinEnergy density

Abstract

fetched live from OpenAlex

Worldwide efforts to develop sodium-ion batteries have accelerated in recent years. There are several companies that are developing sodium-ion batteries, including CATL, HiNa, LiFUN, Natron, Tiamat, Faradion, and UNIGRID. Commercial sodium-ion cells are available at this time, but their energy density may not be sufficient for practical application. Now it is necessary to create sodium-ion cells with a volumetric energy density greater than that of LFP/graphite cells. The volumetric capacity of typical sodium-ion battery negative electrodes like hard carbon is limited to less than 450 mAh/cm 3 . Alloy-based negative electrodes such as phosphorus (P), tin (Sn), and lead (Pb) more than double the volumetric capacity of hard carbon, all having a theoretical volumetric capacity above 1,000 mAh/cm 3 in the fully sodiated state. [1] These alloy materials have massive volume expansion, with P expanding by almost 300% and both Sn and Pb expanding to about 400% of their initial volumes when fully sodiated. We will show that Sn and Pb have excellent half-cell cycling performance despite this large volume change, including high Coulometric efficiency. [2, 3] Pb experiences 387% volume expansion upon full sodiation, which leads to significant changes in the electrode morphology. We will track the morphology of Pb and Pb-hard carbon blended electrodes using SEM imaging. As well, each Na-Pb phase will be examined to analyze their physical properties. These analyses will show that the Pb particles restructure into ~1 µm particles (see Figure 1), even after just a single cycle, and surprisingly do not pulverize the hard carbon in a blended electrode. Single-walled carbon nanotubes appear to be necessary to maintain active material electrical connection. [4] Furthermore, we will show how to improve the formulation of the Pb negative electrode and evaluate capacity retention in half- and full-cell configurations, while investigating the potential degradation mechanisms. The impact of oxide impurities on the cycling stability of Pb negative electrodes will be studied using a controlled heat treatment procedure. The irreversible formation of Na 2 O, a side product of the sodiation reaction of Pb oxide, results in sodium inventory loss, decreased first cycle efficiency, and a detrimental impact on the cell’s long-term cyclability. [5] Additionally, the thermal stability of fully sodiated Pb in the presence of ether-based electrolyte will be investigated. The results suggest that Pb not only provides an energy density advantage, but is also marginally more stable at elevated temperatures compared to hard carbon under the same testing conditions. Overall, we aim to provide valuable insights into the cyclability, degradation mechanisms and thermal stability of Pb, offering useful guidance for its future commercialization and deployment in sodium-ion batteries. [6] References [1] V. Chevrier and G. Ceder J. Electrochem. Soc. 158 (2011). [2] T. R. Jow and L. W. Shacklette J. Electrochem. Soc. 136 1 (1989). [3] M. Garayt, = L. Zhang, = Y. Zhang, = M. Obialor = et al. J. Electrochem. Soc. 171 070523 (2024). [4] M. Garayt et al. J. Electrochem. Soc. 171 120521 (2024). [5] M. Obialor et al. submitted (2025). [6] M. Obialor et al. manuscript in preparation (2025). Figure 1. SEM images of an ion-mill cross-section of a pristine Pb electrode (a) and desodiated Pb electrode after 106 cycles (b). Figure 1

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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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.026
Threshold uncertainty score0.087

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0260.013

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 head0.009
GPT teacher head0.233
Teacher spread0.224 · 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
Published2025
Admission routes1
Has abstractyes

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