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Record W4285497489 · doi:10.1149/ma2022-012412mtgabs

(Invited) Engineered Electrode Materials Via Dry Processing

2022· article· en· W4285497489 on OpenAlexaff
M. N. Obrovac

Bibliographic record

VenueECS Meeting Abstracts · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicRecycling and Waste Management Techniques
Canadian institutionsDalhousie University
Fundersnot available
KeywordsMaterials scienceParticle (ecology)NanoparticlePorosityGraphiteAnodeNanotechnologyCathodeCeramicCoatingParticle sizeChemical engineeringBattery (electricity)Composite materialElectrodeChemistry

Abstract

fetched live from OpenAlex

Dry bulk processing methods can offer surprising control of particle shape, size, internal composition, and surface morphology. Mechanofusion is a method that can be used to smooth, spheronize, coat, and embed particles [1–4]. Dry particle microgranulation (DPMG) is a newly developed mechanofusion method in which ~50 μm speres are added to the process as "templating" media [5]. Here it will be shown that DPMG and new applications of mechanofusion offer unprecedented control in particle shape, internal porosity, and internal composition variation, enabling the creation of never before possible highly engineered particles, including: consolidating submicron particles into dense and uniform ~10 μm particles that are spherical or tetrahedral in shape spontaneous formation of core/shell particles from a mixture of submicron particles with different compositions creating spherical graphite particles from fine flake graphite embedding nanoparticles within the core of ~10 μm host particles applying fused dense oxide particle coatings smoothing the surface of polycrystalline particles to lower their surface area Such methods can produce no waste and operate at 100 % yields, greatly reducing environmental impact and cost compared to other commercial particle production methods. This offers tremendous opportunity for creating sustainable advanced battery materials. Examples of new cathode and anode materials synthesized by these methods and opportunities for new materials will be discussed. References [1] L. Zheng, T.D. Hatchard, M.N. Obrovac, A high-quality mechanofusion coating for enhancing lithium-ion battery cathode material performance, MRS Commun. 9 (2019) 245–250. doi:10.1557/mrc.2018.209. [2] Y. Cao, T.D. Hatchard, R.A. Dunlap, M.N. Obrovac, Mechanofusion-derived Si-alloy/graphite composite electrode materials for Li-ion batteries, J. Mater. Chem. A. 7 (2019) 8335–8343. doi:10.1039/C9TA00132H. [3] L. Zheng, C. Wei, M.D.L. Garayt, J. MacInnis, M.N. Obrovac, Spherically Smooth Cathode Particles by Mechanofusion Processing, J. Electrochem. Soc. 166 (2019) A2924–A2927. doi:10.1149/2.0681913jes. [4] Y. Liu, M. Charlton, J. Wang, J.C. Bennett, M.N. Obrovac, Si 85 Fe 15 O x Alloy Anode Materials with High Thermal Stability for Lithium Ion Batteries, J. Electrochem. Soc. 168 (2021) 110521. doi:10.1149/1945-7111/ac3163. [5] M.N. Obrovac, L. Zheng, M.D.L. Garayt, Engineered Particle Synthesis by Dry Particle Microgranulation, Cell Reports Phys. Sci. 1 (2020) 100063. doi:10.1016/j.xcrp.2020.100063.

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 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 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 score0.105
Threshold uncertainty score0.650

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.008
GPT teacher head0.217
Teacher spread0.209 · 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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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