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Record W4416611837 · doi:10.1149/ma2025-023462mtgabs

Embedded Nanoparticle Composites as Li-Ion Battery Anode Materials

2025· article· W4416611837 on OpenAlexaff
Sabina Yasmin, Tarelle Sterling, M. N. Obrovac

Bibliographic record

VenueECS Meeting Abstracts · 2025
Typearticle
Language
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsDalhousie University
Fundersnot available
KeywordsAnodeComposite numberGraphiteElectrolyteMicrostructureNanoparticleBattery (electricity)Particle (ecology)

Abstract

fetched live from OpenAlex

Graphite is used as an anode material in nearly all commercial Li-ion Batteries (LIBs). To increase energy density, Si and Sn-based materials (including SiO x ) are intensively being investigated as anode materials. If used to their full theoretical extent, they could increase LIB energy density by as much as 20%. 1 However, the widespread application of these elements is impeded due to their large volume change and unstable solid electrolyte interphase (SEI) formation that leads to capacity fade. A variety of design approaches have been taken to make alloy anodes compatible with commercial cells. 2-4 However, in most cases, the preparation procedures are complex, give low yields, and require expensive chemicals, making their large-scale production prohibitively expensive. Recently, we have shown that mechanofusion can be used to embed natural graphite (NG) with Si-nanoparticles (Si-NP). 5 In this process, the Si-NPs filled voids in the NG that had connections to the NG particle surface. The resulting Si-NP/NG composite particles had increased capacity and capacity retention even when no electrolyte additives were used. These results were highly promising. However, many unknowns remain, including: how the composite morphology and microstructure depend on the mechanofusion conditions, how many NPs can be inserted into NG, and how this process depends on the NP size. In this study, TiO 2 -NPs (rutile phase) were used as model guest particles to make TiO 2 -NP/NG composites and study the embedding process of NP into graphite. TiO 2 was selected for this study, since it is available in NPs of many different sizes and it has low electrochemical activity, allowing the electrochemistry of the NG to be studied after the embedding process. Fig. 1(a) and 1(b) show cross-section SEM images of NG embedded with 100 nm and 300 nm TiO 2 -NPs, respectively. It was found that mechanofusion conditions and NP size have a profound effect on the final composite morphology and microstructure. Importantly, the host graphite was found to retain high crystallinity under mechanofusion conditions that induce TiO 2 guest particle embedding, resulting also in good electrochemical performance of the host graphite. Moreover, the NP size was found to determine the porosity in the loading of the composites, with smaller NP size leading to increased NP loading and reduced internal porosity. These results demonstrate design strategies to NP/NG composite particles that can lead to new high energy density anode materials. Moreover, the dry processing method is cheap, scalable, and does not produce waste. Acknowledgements The authors acknowledge funding from NSERC and NOVONIX Battery Technology Solutions under the auspices of the NSERC Alliance grants program. References: N. Obrovac and V. Chevrier, Chemical Reviews , 114 , 11444-11502 (2014). Liu, Z. Lu, J. Zhao, M. T. McDowell, H. Lee, W. Zhao and Y. Cui, Nature Nanotechnology , 9 , 187-192 (2014). Wang, J.H. Ahn, J. Yao, S. Bewlay and H. Liu, Electrochemistry Communications , 6 , 689-692 (2004). Liu, H. Wu, M. T. McDowell, Y. Yao, C. Wang and Y. Cui, Nano Letters , 12 , 3315-3321 (2012). He, M. Salehabadi, S. Yasmin and M. N. Obrovac, Journal of the Electrochemical Society , 170 , 120511 (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)0.000
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.012
GPT teacher head0.261
Teacher spread0.249 · 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
Published2025
Admission routes1
Has abstractyes

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