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Record W2255132309 · doi:10.1149/ma2015-02/7/522

NixSi1-x Alloy Negative Electrodes for Li-Ion Batteries

2015· article· en· W2255132309 on OpenAlexaff
Zhijia Du, R. A. Dunlap, M. N. Obrovac

Bibliographic record

VenueECS Meeting Abstracts · 2015
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsDalhousie University
Fundersnot available
KeywordsMaterials scienceElectrolyteAlloyLithium (medication)Diethyl carbonateSputteringThin filmElectrodeAnodeElectrochemistryFOIL methodChemical engineeringEthylene carbonateMetallurgyComposite materialNanotechnologyChemistry

Abstract

fetched live from OpenAlex

Introduction Si containing alloys are promising candidates as anode materials for lithium ion batteries. This is owing to the high theoretical capacity of Si (2194 Ah/L, compared to 764 Ah/L for graphite) [1]. Pure Si, however, suffers from severe volume expansion (280% in the fully lithiated state) during the lithiation/delithiation process [2]. High internal stress in the electrode can lead to particle cracking, poor electrical contact and thus poor cycling stability. The use of active/inactive composites is an efficient way to reduce volume expansion and improve cycling performance [1,3]. In the present study, NixSi1-x (0≤x≤0.65) thin film libraries were synthesized by combinatorial sputtering. A structural and compositional analysis of the films and the effect of composition on their electrochemical behavior are presented. It was found that the capacity of NixSi1-x thin films could not be explained by the lithiation mechanisms proposed in any of the previous studies of Si-TM films. Instead, it was found that increasing Ni content suppressed the average lithiation voltage, resulting in capacity reduction, however all the Si in the alloy remained active. Experimental Thin film libraries of Ni-Si were fabricated using sputtering. Cu foil discs with sputtered thin films were assembled in 2325-type coin cells with Li metal foil (99.9%, Sigma-Aldrich) counter electrodes. 1M LiPF6 (BASF) in a solution of ethylene carbonate, diethyl carbonate and monofluoroethylene carbonate (volume ratio 3:6:1, all from Novolyte Technologies) was used as electrolyte. Cells were galvanostatically cycled at 30.0 ± 0.1 °C between 5 mV and 0.9 V with a Maccor Series 4000 Automated Test System. Results Selected XRD patterns of two Ni-Si thin films are shown in Figure 1. All the patterns have two broad diffraction peaks at around 28° and 49°, indicating the amorphous nature of sputtered thin films. With the increase of Ni content, the intensity of the peak at 49° increases and its position shifts slightly to lower angle. Figure 2 shows the differential capacity curves of the same Ni-Si thin films shown in Figure 1. Two broad peaks during lithiation (denoted as peak A and B) and two corresponding peaks during delithiation (denoted as peak A' and B') can be clearly observed for the Ni0.04Si0.96 thin film. The two lithiation peaks shift to less positive voltages for Ni0.35Si0.65 thin film while the B' peak position remains unchanged. The apparent suppression in discharge voltage by increasing Ni content causes a reduction in the film capacity. This would suggest that some of the Si is becoming inactive, but this is not the case. While peak B becomes truncated by the voltage suppression the capacity of peaks A/A' remain unaffected and therefore can be used as a measure of active Si in the alloy. Figure 3 shows the percent active Si present in the Ni-Si films calculated on the basis of the capacity under peak A'. According to this model all Si atoms are active to Li. Above x = 0.4 the amount of active Si decreases only because peak A also becomes truncated for these compositions. The model reported by Fleischauer et al. [4] does not explain the behavior observed here and is also shown in Figure 3 for comparison. Here the electrochemistry of Ni-Si films will be discussed. It will be suggested the lithiation voltage suppression observed in these films arises from internal stress that arises between the expanding Si active phase and the inactive Ni phase. This has important consequences for other Si/inactive alloy negative electrodes. References [1] M.N. Obrovac, L. Christensen, Dinh Ba Le, and J.R. Dahn, J. Electrochem. Soc., 154, A849 (2007). [2] M. N. Obrovac and L. Christensen, Electrochem. Solid-State Lett., 7, A93 (2004). [3] O. Mao, R.L. Turner, I.A. Courtney, B.D. Fredericksen, M.I. Buckett, L.J. Krause, and J.R. Dahn, Electrochem. Solid-State Lett, 2, 3 (1999). [4] M D. Fleischauer, R. Mar, and J.R. Dahn, J. Electrochem. Soc., 154 (3), A151 (2007). 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 score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.002

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 head0.025
GPT teacher head0.263
Teacher spread0.237 · 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
Published2015
Admission routes1
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