Pulsed Electrodeposition of Nanocatalysts for Energy Storage Applications: Mathematical Modeling and Experimental Studies
Notice bibliographique
Résumé
With the growing demand for clean energy, the need for affordable, efficient energy storage technologies has never been more urgent. Pumped hydro and compressed air storage technologies have been widely used, but they are heavily dependent on location and are not considered to be universal solutions. Secondary batteries, including Li-ion and lead-acid batteries, have played a major role in storing excess electrical energy, but they suffer from high cost, performance degradation over time, and environmental issues during use and disposal. Metal-air batteries have experienced a renaissance on account of their acceptable energy density, superior cycleability, enhanced stability and cost benefits compared with other commercially available batteries. Despite these desirable properties, metal-air batteries suffer from a number of thermodynamics and kinetics hurdles that must be overcome, if their widespread deployment is desired. One of the major impediments to wide-scale commercialization of metal-air batteries continues to be their inferior capacity and cycle stability. Part of these issues can be ascribed to the shortcomings of the cathode materials and structures in general, and the inferiority of their catalyst layers in particular. It is well established that nano-structuring battery components would improve their short- and long-term performance by enhancing reaction kinetics. Nanocatalysts such as transition metals, platinum and manganese dioxide were electrodeposited on a number of conductive substrates, including carbon paper, carbon cloth and titania nanotubes (TNTs) with high aspect ratios. Catalyst electrodeposition was performed utilizing pulsed current electrodeposition with different waveforms, low duty cycles, and high peak deposition current densities. All catalysts were characterized using microscopy, spectroscopy and electrochemical techniques. The presence of nanocatalysts were confirmed by SEM and XRD and were found to be 2-5 nm for platinum and 10-50 nm for nickel and oxides of manganese. A mathematical model was developed to determine the roles of various deposition process parameters on the size and catalytic activity of the resulting nanocatalysts in different environments. The model is based on progressive nucleation and takes into account some key contributing factors towards growth current and, ultimately, nucleation, including diffusion, ohmic and charge transfer phenomena. A varying diffusion coefficient during electrodeposition was also considered and evaluated. Model predictions were confirmed by experimental measurements with emphasis on platinum and manganese dioxide nanocatalysts for use in PEM fuel cells and metal-air batteries, respectively.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 source (Gemma direct ou Codex distillé), 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 ».