Improving 2D Hybrid Energy Storage Electrode Homogeneity Via Graphene Oxide/Active Nanomaterial Electrophoretic Co-Deposition
Bibliographic record
Abstract
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 (Li 4 Ti 5 O 12 , LTO) or titanium niobate (TiNb 2 O 7 , 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 Li 4 Ti 5 O 12 /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).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".