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Record W4391639872 · doi:10.1149/ma2023-022308mtgabs

Morphology Control of Ball Milled Si-Ti Alloys and a Comparison of Their Surface Reactivity with SiO<sub>x</sub>

2023· article· en· W4391639872 on OpenAlexaff
Mina Salehabadi, M. N. Obrovac

Bibliographic record

VenueECS Meeting Abstracts · 2023
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloy Microstructure Properties
Canadian institutionsDalhousie University
Fundersnot available
KeywordsMaterials scienceMorphology (biology)Chemical engineeringBall millCrystallographyMetallurgyChemistry

Abstract

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Si-based anode materials have been widely studied due to their higher volumetric capacity compared to graphite.1 However, they can suffer from poor cycling due to severe volume changes, crystallization effects (formation of Li15Si4), an unstable solid electrolyte interphase (SEI) and surface erosion. Alloying Si with an electrochemically inactive material can decrease the volume expansion and lead to better cycling performance. If nanostructured or amorphous Si-based alloys are used, the phase transition due to the crystallization effect is suppressed.1,2 Considering all improvements, Si-based anode materials still experience large surface erosion that continuously increases the surface area of active material particles during cycling. Different studies show that the main causes of surface erosion are the volume changes during cycling and the reaction between the active material and electrolyte.3,4 It is known that higher active material surface area leads to a higher reaction rate with electrolyte. This leads to a larger lithium inventory loss and a decrease in coulombic efficiency (CE), which is undesirable.5 Mechanical milling has long been used to create metal alloys. Cao et al showed that preparing Si-based alloys with a two-step milling method, in which Si is first milled alone with the metal added in the second step, produces Si-M alloys having an amorphous active Si-phase and a reduced surface area compared to conventional one-step milling.5 SiOx has also been extensively studied as an anode material because of reduced volume expansion, less side reactions with electrolyte, and high capacity retention during cycling.6 Despite decades of research to our knowledge, it has yet to be determined how the surface erosion or reactivity of SiOx compares with Si-based alloys with a similar surface area. This fundamental question needs to be answered if Si-based alloys are to be considered as candidates for practical application as negative electrode materials. A barrier to answering this question has been the very different surface areas of Si-based alloys (typically high surface area materials prepared by ball milling) and SiOx, which has a low surface area. Increasing the surface area of SiOx and reducing the surface area of the Si-based alloys using the two-step milling method can provide the opportunity to compare their surface reactivity. In this study, SixTi100-x (x= 85,70, 60) alloys were prepared by ball milling using the one-step and two-step methods, allowing low and high surface area alloys to be synthesized. The results show that reducing alloy surface area results in an increased CE, however, the difference is not pronounced. The surface erosion or reactivity of low surface area Si0.85Ti0.15 was compared with SiOx with a similar surface area. Cycling results show that Si0.85Ti0.15 has a higher CE than SiOx in initial cycles, but SiOx has a markedly higher CE after longer term cycling. These results are verified by direct observations of reduced surface erosion on cycled SiOx alloy particles compared to Si0.85Ti0.15 particles. Figure 1(a) and (b) shows the cross-section SEM images of low surface area Si0.85Ti0.15 and SiOx with a similar surface area after 50 cycles, respectively. The SiOx particles are almost pristine, while the Si0.85Ti0.15 particles are severely eroded. These results imply that the chemistry of SiOx plays a more important role in its good cycling performance than does its low surface area. References. Obrovac, M. N. & Chevrier, V. L. Alloy Negative Electrodes for Li-Ion Batteries. Chem Rev 114, 11444–11502 (2014). Iaboni, D. S. M. & Obrovac, M. N. Li15Si4 Formation in Silicon Thin Film Negative Electrodes. J Electrochem Soc 163, A255 (2016). Chevrier, V. L., Aiken, C. & Krause, L. J. Impact of Electrolyte on the Cycling of Si-Based Materials. in 228th ECS Meeting – Phoenix (2015). 4.Krause, L. J., Brandt, T., Chevrier, V. L. & Jensen, L. D. Surface Area Increase of Silicon Alloys in Li-Ion Full Cells Measured by Isothermal Heat Flow Calorimetry. J Electrochem Soc 164, A2277 (2017). Cao, S., Tahmasebi, M. H., Gracious, S., Bennett, J. C. & Obrovac, M. N. Preparation of Low Surface Area Si-Alloy Anodes for Li-Ion Cells by Ball Milling. J Electrochem Soc 169, 060540 (2022). Liu, Y., Charlton, M., Wang, J., Bennett, J. C. & Obrovac, M. N. Si85Fe15Ox Alloy Anode Materials with High Thermal Stability for Lithium Ion Batteries. J Electrochem Soc 168, 110521 (2021). 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)0.000
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 score0.003

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 head0.211
Teacher spread0.199 · 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
Published2023
Admission routes1
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