Improving 2D Hybrid Energy Storage Electrode Homogeneity Via Graphene Oxide/Active Nanomaterial Electrophoretic Co-Deposition
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».