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Record W2277754601 · doi:10.1149/ma2015-02/7/515

Rapid Milling of Alloy Anodes

2015· article· en· W2277754601 on OpenAlexaffabout
Timothy Hatchard, A.N. Genkin, M. N. Obrovac

Bibliographic record

VenueECS Meeting Abstracts · 2015
Typearticle
Languageen
FieldEngineering
TopicWelding Techniques and Residual Stresses
Canadian institutionsDalhousie University
Fundersnot available
KeywordsAnodeMaterials scienceAlloyBattery (electricity)CathodeMetallurgyElectronicsElectrodeElectrical engineering

Abstract

fetched live from OpenAlex

Li-ion batteries have long been used as the energy source for portable electronics. It is also the battery of choice for the burgeoning electric vehicle industry. The first commercial Li-ion cell used a LiCoO2 cathode and a carbonaceous anode. Only recently have cell makers introduced new materials, such as NMC type cathodes used in many cells today. In most cases the anode is still graphite, despite decades of research on new materials with higher energy density. Much of the research in new anode materials has focused on alloys, usually of Si due to its ability to alloy with large amounts of Li. However, problems persist that keep alloy anodes from widespread use in commercial cells. The biggest issue for the implementation of alloy anodes is their huge volume expansion during lithiation that can lead to particle fracture, pulverization and electrical isolation of portions of the anode, disruption of the SEI and ultimately capacity loss and cell failure [1]. It has been shown that using amorphous or nanostructured alloys can alleviate the pulverization experienced by crystalline materials that suffer from inhomogeneous expansion during phase changes [2]. Also, the inclusion of inactive phases in alloys can help to lessen the expansion of alloy anodes during lithiation, thus easing the stress on the electrode and reducing capacity fade [3]. Mechanical milling has long been used to both reduce grain size and create metal alloys. Ferguson et al. [4,5] used mechanical attrition to make nanostructured Sn-Co-C alloys. These alloys had improved electrochemical performance versus crystalline counterparts of similar composition. However, the process of mechanical attrition can be very time consuming, with some experiments taking days or weeks to produce the desired structure. Typically alloys are made on the lab scale by SPEX milling. Despite decades of research, to our knowledge this method has not been optimized. In fact the SPEX milling conditions commonly used to make alloy anodes are far from optimal and furthermore do not allow the use of soft metals (such as Sn) as starting materials [4]. The purpose of this work was to develop a lab-scale milling method to rapidly produce amorphous or nano-structured materials, and to produce in hours alloys that can take days or weeks to produce with other milling techniques. To accomplish this, 325 mesh Si (Aldrich) was SPEX milled under many different conditions, varying ball size/amount, sample size/amount, temperature, and time. Optimized conditions were then used to synthesize Si and Sn-based alloys. Conditions vastly superior to those previously reported for producing amorphous phases by SPEX milling were found. Most importantly, it was discovered that a reduction in ball size caused the grain size of Si to reduce dramatically during milling. Figure 1 shows a comparison of the Si (111) XRD peak of 325 mesh Si (Aldrich) after being SPEX milled with different conditions. The optimized method leads to a tremendous reduction in the grain size of the Si. Given the success of the optimized SPEX milling method in Si grain size reduction, the question remains if the technique can rapidly produce nano-structured alloys. Figure 2 shows the XRD pattern for a Sn30Co30C40 alloy produced by attrition, reported by Ferguson et al. [4]. Elemental Sn powder could not be used by this method, as soft elements, like Sn, would weld together in the attritor [4]. Instead, the Sn and Co were pre-alloyed to form CoSn2 by arc melting and annealing which added extra processing steps. In contrast, Sn-Co-C alloys could be successfully prepared simply from elemental Sn, Co and graphite powders using the optimized SPEX milling method. An XRD pattern of the Sn30Co30C40 alloy prepared by this method is also shown in Figure 2. Both methods produce an amorphous/nanocrystalline alloy. However, the attritor method took 16 hours to produce the alloy with the XRD pattern shown, while the SPEX mill sample was produced in 3.5 hours. This technique can greatly speed the screening of alloy candidates. A description of the rapid SPEX milling method, microstructure and electrochemistry of some of the alloys produced will be presented and compared to results for alloys produced with more time consuming methods. References. [1] M.N. Obrovac and V.L. Chevrier, Chem. Rev. 114, 11444 (2014). [2] L.Y. Beaulieu et. al., J. Electrochem. Soc., 150(11), A1457 (2003). [3] Ou Mao and J.R. Dahn, J. Electrochem. Soc., 146 (2), 423 (1999). [4] P.P. Ferguson et al., Journal of Power Sources, 194, 794 (2009). [5] P.P. Ferguson, Ph.D. Thesis, Dalhousie University, Halifax, NS, Canada (2009). [6] Ou Mao et al., J. Electrochem. Soc., 146 (2), 405 (1999). 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)0.001
metaresearch head score (Gemma)0.001
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: none
Teacher disagreement score0.008
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.005

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 head0.030
GPT teacher head0.241
Teacher spread0.210 · 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 routes2
Has abstractyes

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