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Record W2308546891 · doi:10.1149/ma2014-02/5/429

Synthesis and Electrochemical Performance of Low Surface Area Alloy Anodes for Lithium-Ion Battery

2014· article· en· W2308546891 on OpenAlexaffabout
Xiuyun Zhao, R. A. Dunlap, M. N. Obrovac

Bibliographic record

VenueECS Meeting Abstracts · 2014
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsDalhousie University
Fundersnot available
KeywordsMaterials scienceDiffractometerScanning electron microscopeElectrolyteElectrodeAlloyElectrochemistryBall millParticle sizeLithium-ion batterySpecific surface areaAnalytical Chemistry (journal)MetallurgyChemical engineeringComposite materialBattery (electricity)Chemistry

Abstract

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Introduction Alloy negative-electrode materials could significantly increase the energy density of commercial Li-ion batteries [1]. From a practical point of view, alloys prepared by ball milling are inexpensive lithium hosts and easily scalable. However, such alloys typically have small particle sizes and high surface area, increasing their reactivity with electrolyte. Here, we describe a composite alloy material which has been post treated to significantly reduce surface area. The resulting composites have significantly reduced surface area and improved cycling performance over ball milled alloys of similar composition. Experimental Alloys were prepared ball milling stoichiometric amounts of Si and Fe. Post-treated alloys were ground and sieved to a 53 μm particle size. Electrodes comprising active material, carbon black and polyimide binder were cycled in 2325 coin-type cells with two Celgard 2300 separators, a lithium foil counter/reference electrode and 1M LiPF6 in EC/DEC/FEC 60/30/10 by volume electrolyte at 30 °C. Cells were cycled at a C/4 rate and trickled discharged to C/20 in a voltage range of 0.005 V-0.9 V. X-ray diffraction (XRD) measurements were collected using a Rigaku Ultima IV diffractometer with a Cu Kαsource. A Phenom G2-pro Scanning Electron Microscope (SEM, Nanoscience, Arizona) was used to study the particle size and morphology of the samples. Surface area was determined by single-point Brunauer, Emmett, and Teller (BET) method using a Micromeritics Flowsorb II 2300 surface area analyzer. Results Figure 1 shows the XRD pattern of ball-milled Si/Fe alloy, which consists of XRD peaks from Si, Fe and FeSi2 phases. Figure 2 shows the SEM images of the Si/Fe and post-treated alloy particles. For Si/Fe, many fine particles with a diameter less than 3 μm can be observed along with a few large particles. The particle size range for the post-treated particles is between 10 and 40 μm. BET surface area measurement shows that the post treated particles have a significantly reduced surface area compared to the Si/Fe ball milled alloy (2.6 m2/g for Si/Fe, 0.9 m2/g for post-treated particles), which is consistent with the SEM results. Figure 3 shows the cycling performance of Si/Fe and the post-treated particles. The post-treated particles have a reversible capacity of 600 mAh/g after the first cycle and good capacity retention. By contrast, the capacity of the ball milled Si/Fe alloy electrode fades gradually during cycling. Acknowledgements The authors acknowledge funding from NSERC and 3M Canada, Co. Xiuyun Zhao acknowledges the support from the DREAMS program. References [1]. M.N. Obrovac, Leif Christensen, Dinh Ba Le and J.R. Dahn, J. Electrochem. Soc. 154, A849 (2007).

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 score0.003

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)0.0010.001

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 head0.215
Teacher spread0.205 · 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".

Quick stats

Citations0
Published2014
Admission routes2
Has abstractyes

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