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Record W3025827587 · doi:10.1149/ma2020-012352mtgabs

Composites of Silicon@Li<sub>4</sub>Ti<sub>5</sub>O<sub>12 </sub>and Graphite for High-Capacity Lithium-Ion Battery Anode Materials

2020· article· en· W3025827587 on OpenAlexaff
James Sturman, Yong Zhang, Chae-Ho Yim, Svetlana Niketic, Mathieu Toupin, Elena A. Baranova, Yaser Abu‐Lebdeh

Bibliographic record

VenueECS Meeting Abstracts · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsNational Research Council CanadaUniversity of Ottawa
Fundersnot available
KeywordsMaterials scienceGraphiteSiliconAnodeLithium (medication)ElectrodeGravimetric analysisBattery (electricity)Lithium-ion batteryComposite materialChemical engineeringCarbon fibersNanotechnologyComposite numberOptoelectronicsChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

The application of lithium-ion batteries to long-range electric automobiles requires negative electrode materials with a higher specific capacity than traditional graphite. Next-generation materials should have both a high gravimetric capacity and capacity retention upon cycling. Silicon is a promising material for the negative electrode as it has a theoretical capacity nearly 10 times greater than graphite (3579 mAh/g for Li 15 Si 4 ) (1). However, pure silicon (Si) undergoes severe mechanical stresses and pulverization during lithiation and delithiation (2). In contrast, Li 4 Ti 5 O 12 (LTO) is mechanically stable and has a high rate capability. This makes it suitable to buffer the volumetric expansion of silicon when integrated into a core/shell design. In this study, a facile sol-gel technique was used to treat silicon nanoparticles and create a Si-core @ LTO-shell by modifying a procedure described previously (3). These silicon nanoparticles retain the high capacity associated with Li-Si alloys, but the pulverization of the particles is reduced. Further improvement to the cycle stability of the Si/LTO composite was achieved with the addition of graphite to the electrode material. The graphite not only provides capacity, but it is highly stable and helps buffer some of the volumetric expansion of the silicon-rich electrode material (2). The combination of graphite (G) and nano silicon/LTO (Si@LTO) is therefore an economical and scalable approach to increase the energy density of lithium-ion batteries. The electrode composites were tested in half cells and were cycled at C/10 and 1C. The cycling performance of Si@LTO + G revealed an initial high capacity (~860 mAh/g at C/10 and ~650 mAh/g at 1C) and good capacity retention (~75% after 90 cycles at 1C). In contrast, a silicon/graphite (Si/G) composite retained only 35% of its capacity at 1C under the same conditions. Compared to Si/G, the Si@LTO + G composites had lower capacity fade by reducing both silicon pulverization and SEI formation. This was confirmed by a reduction in the impedance of the Si@LTO + G batteries after cycling. Figure 1 illustrates the difference in cycle performance between Si/G and Si@LTO + G electrodes. The Si@LTO + G composite had a better rate capability when the charging rate was increased from C/10 to 1C. At 1C, the Si/G composite immediately lost 50% of its capacity. In contrast, the Si@LTO + G composite immediately lost only 25% of its capacity. This value is similar to the capacity retention of a graphite half cell tested under the same conditions (~372 mAh/g at C/10 and ~280 mAh/g at 1C). The electrode powders were characterized with X-Ray Diffraction (XRD), Scanning Electron Microscopy (SEM), Energy Dispersive X-ray Spectroscopy (EDX), and Transmission Electron Microscopy (TEM). XRD of the raw Si@LTO powder revealed the presence of crystalline silicon and Li 4 Ti 5 O 12. The core-shell structure was confirmed with TEM, and EDX revealed a uniformed distribution of the silicon, titanium, and oxygen. The coin cells were characterized with operando XRD using a modified coin cell and coin cell holder designed in-house. This technique was used to observe changes in the phases of the graphite, silicon, and LTO shell. These silicon@LTO + graphite composites prove to be promising for the development of stable and high-energy-density lithium-ion batteries. Figure 1: (a) Normalized delithiation capacity vs. cycle number for both silicon/graphite and core-shell silicon@LTO with graphite. First 10 cycles at a charging rate of C/10, followed by 90 cycles at 1C. (b) Animation of a silicon particle (top) and an ideal Si@LTO particle (bottom) upon lithiation. References 1. M. Wetjen et al, J. Electrochem. Soc ., 164 , A2840 (2017). 2. C. Yim, F. Courtel, and Y. Abu-Lebdeh. J. Mater. Chem. A. , 1 , 8234 (2013). 3. J. Lee et al . Energy Environ. Sci., 8 , 2075 (2015). Figure 1

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.024
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.018
GPT teacher head0.214
Teacher spread0.196 · 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 teacher head, not a consensus.

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
Published2020
Admission routes1
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