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Record W3024649729 · doi:10.1149/ma2020-015597mtgabs

Improving 2D Hybrid Energy Storage Electrode Homogeneity Via Graphene Oxide/Active Nanomaterial Electrophoretic Co-Deposition

2020· article· en· W3024649729 on OpenAlexaffabout
Marianna Uceda, Karim Zaghib, George P. Demopoulos

Bibliographic record

VenueECS Meeting Abstracts · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsHydro-QuébecMcGill University
Fundersnot available
KeywordsElectrophoretic depositionGrapheneMaterials scienceElectrodeNanomaterialsNanotechnologySupercapacitorOxideChemical engineeringCoatingCapacitanceChemistry

Abstract

fetched live from OpenAlex

Lithium-ion battery (LIB) electrode performance is directly related to charge movement within the electrode material. Improving electrode kinetics involves lowering the internal resistance which may be achieved through 1) nanosizing the active material to decrease the lithium-ion diffusion path; 2) using highly conductive additives; and 3) homogeneous microstructuring to form an effective percolation network for electron and ion conductive pathways. Among these highly conductive additives is the 2D graphene, or reduced graphene oxide (rGO, so-called due to its synthetic pathway). Which, thanks to its few-layers of densely packed sp2 hybridized carbon atoms with delocalized electrons, has excellent mechanical properties and electronic conductivity. However, achieving the desired homogeneity between active and conductive components is a challenge for the conventional tape-casting technique – particularly when nanosized and/or 2D nanomaterials are involved. The nano and 2D nature of the materials will present rheological challenges during the casting of the electrode slurry and result in mesoscale aggregation which is a property that triggers, or further accelerates, battery degradation. Thus, the benefits of nanosizing and using graphene are lost. Electrophoretic deposition (EPD) is a novel electrocoating technique capable of assembling coatings from a stable suspension through the application of an electric field. It is well accepted that EPD has excellent self-assembling capabilities and herein lies its advantage to being used to fabricate composite lithium-ion electrodes. EPD provides a simplified coating technique with short process times. Moreover, the versatility of the suspension also allows EPD to be potentially environmentally friendly – a property not available to the tape casting technique due to its use of the highly toxic N-Methyl-2-pyrrolidone solvent. The challenge of electrophoretically depositing graphene is that a stable graphene suspension is difficult to form due its strong propensity for interaction between sheets. Thus, this problem may be sidestepped by using graphene oxide (GO) which is a functionalized graphene derivative. The functional groups located on the graphene surface provide electrostatic repulsion which prevents aggregation during dispersion and also allows the use of more polar solvents. With this in mind, our McGill HydroMET group has successfully used EPD to fabricate binder-free composite electrodes with rGO as conductive material and lithium titanate spinel (Li4Ti5O12, LTO) or titanium niobate (TiNb2O7, TNO) as the nanosized active material. This was accomplished through co-deposition of conductive and, in the case of LTO, active material precursor followed by high temperature annealing to induce transformation of GO to rGO (and transformation of LTO precursor to the final spinel LTO) (Uceda, M., Chiu, H.-C., Gauvin, R., Zaghib, K., Demopoulos, G. P. (in press) Electrophoretically co-deposited Li4Ti5O12/reduced graphene oxide nanolayered composites for high-performance battery application. Energy Storage Materials). Both types EPD systems are then compared to conventionally casted electrodes through electrochemical testing and physical characterization. In both cases, EPD is shown to provide superior homogeneity which results in improved electrode kinetics and battery performance. Acknowledgments: This research was supported by Hydro-Quebec/NSERC grants and the McGill Sustainability Systems Initiative (MSSI).

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 score0.004

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 communication0.0000.001
Open science0.0000.001
Research integrity0.0010.000
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 head0.006
GPT teacher head0.205
Teacher spread0.198 · 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
Published2020
Admission routes2
Has abstractyes

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