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Record W2345626459 · doi:10.1149/ma2016-03/2/1010

Nanostructured Si Alloys Via Ethanol Delithiation

2016· article· en· W2345626459 on OpenAlexaff
Leyi Zhao, M. N. Obrovac

Bibliographic record

VenueECS Meeting Abstracts · 2016
Typearticle
Languageen
FieldEngineering
TopicSilicon and Solar Cell Technologies
Canadian institutionsDalhousie University
Fundersnot available
KeywordsMaterials scienceAmorphous solidChemical engineeringOxidizing agentElectrodeElectrochemistrySiliconVoid (composites)Ball millNanotechnologyMetallurgyComposite materialCrystallographyPhysical chemistry

Abstract

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Introduction Si-based alloys are potential candidates as high energy density negative electrodes in Li-ion batteries. Such alloys have high theoretical capacity but also have high volume expansion, which can lead to cell fade. It has been shown previously that amorphous Si (a-Si) synthesized by chemical or physical deposition has improved cycling performance compared to crystalline Si (cr-Si) [1, 2]. Typically, a-Si is made by ball milling or atomic deposition techniques. Templating methods have also been employed to make nanostructured Si alloys that include void space in which the Si can expand [3, 4]. Such alloys can perform well as negative electrodes in Li cells. An inexpensive route for synthesizing bulk quantities of a-Si or nanostructured Si alloys is desirable. In this study, a new chemical delithiation method employing ethanol as an oxidizing agent was applied to prepare bulk quantities of a-Si from Li-Si compounds. The a-Si formed was found to have a unique exfoliated layered structure, which has lower volume expansion than cr-Si and improved cycling characteristics. C-Si and Fe-Si alloys were also synthesized by the delithiation of C-Li-Si and Fe-Li-Si alloys using this chemical delithiation method. When tested as negative electrodes in Li cells, the C-Si and Fe-Si alloys showed superior electrochemical characteristics and lower volume expansion than cr-Si. Experimental Li 12 Si 7 , Li 7 Si 3 , Li 13 Si 4 or Li 22 Si 5 compounds were first prepared in an arc furnace from the elements. Ethanol was then used to delithiate the Li-Si compounds under an Ar atmosphere. After the resulting slurry was washed with distilled water, a-Si was recovered with a 70% yield. C-Li-Si and Fe-Li-Si precursors with serial C: Si or Fe: Si stoichiometric ratios were prepared by ball milling. Ethanol was then used to delithiate the C-Li-Si or Fe-Li-Si alloys under an Ar atmosphere. After the resulting products were washed with distilled water, C-Si and Fe-Si alloys were recovered with 79% and 70% yields, respectively. Electrode slurries were prepared by mixing active materials (a-Si, C-Si or Fe-Si), carbon black and polyimide in a volume ratio of 62.5/18/19.5 in N-methyl pyrrolidinone. Electrode disks were punched from the coating foil and heated in a tube furnace for 3h at 300 °C under an Ar flow. 2325 coin-type cells were assembled in an Ar-filled glovebox with a Li counter/reference electrode. All cells were cycled between 5~900 mV with a Maccor Series 4000 Automated Test System. Results a-Si prepared from ethanol delithiation of Li 12 Si 7 , Li 7 Si 3 , Li 13 Si 4 , and Li 22 Si 5 resulted in layered products, except in the case of Li 22 Si 5 , which was composed of dense particles. Figure 1 shows an SEM image of a-Si prepared from ethanol delithiation of Li 12 Si 7 . a-Si prepared from ethanol delithiation of Li 12 Si 7 had the most orderly layered structure, with the layers being highly exfoliated. All a-Si samples had superior cycling performance and lower volume expansion compared to cr-Si. Figure 2 shows the cycling performance of an a-Si electrode prepared from ethanol delithiation of Li 12 Si 7 and a cr-Si electrode. It is thought that the porous layered structure of these delithiated materials can accommodate the Si volume expansion during lithiation, resulting in low overall particle expansion and improved cycling performance. Remarkably, C-Si and Fe-Si alloys made by the delithiation of ball milled C-Li-Si or Fe-Li-Si alloys contained very low silicide or carbide content. Instead the alloys were primarily composed of a nano-composite of elemental C and Si or Fe metal and Si. This was confirmed by X-ray diffraction and Mössbauer spectroscopy. These alloys have a completely different nanostructure than conventional ball milled Si-based alloys, in which carbon and Fe are typically completely reacted to form carbides and silicides. Conclusions The alcohol delithiation method represents an effective means of producing Si-based alloys as negative electrode materials for Li cells. The unique nanostructures of these alloys and their electrochemical performance will be discussed. References [1] L. B. Chen, J. Y. Xie, H. C. Yu, and T. H. Wang, J. Appl. Electrochem ., 39, 1157 (2009). [2] R. Epur, M. Ramanathan, F. R. Beck, A. Manivannan, and P. N. Kumta, Mat. Sci. Eng. B-Solid , 177, 1157 (2012). [3] H. Y. Lee and S. M. Lee, J. Power Sources , 112, 649 (2002). [4] M. Yoshio, T. Tsumura, and N. Dimov, J. Power Sources , 146, 10 (2005). 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.039
Threshold uncertainty score0.464

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.010
GPT teacher head0.207
Teacher spread0.197 · 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.

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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Published2016
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