Fabrication Characteristics and Performance Enhancement of Nb18W16O93 and MoNb12O33 Nanowires for Lithium-Ion Batteries Application
Notice bibliographique
Résumé
To date, graphite is widely employed as an anode material for Lithium-ion batteries (LiBs) because it has demonstrated superior cycling stability and high specific capacity in comparison with other potential anode materials. However, the use of graphite as an anode material in LiBs has been limited by its safety concern as well as low energy density. Thus, it is imperative to develop a new anode material to address these shortcomings. To this end, niobium-based oxide nanowires had been proposed as one of the alternative materials as a potential anode for LiBs. These materials have demonstrated high theoretical capacity, significant structural stability, high power density, and environmental friendliness. Furthermore, the enhanced performance of nanowires compared to their bulk counterparts as a material for LIBs anodes has motivated researchers to focus more attention on nanowires. Nevertheless, the kinetics of electrochemical reactions in these compounds is hindered by their intrinsically poor electronic conductivity and electron transfer properties. These tend to be significant flaws restricting their practical use in LIBs. More so, it is desirable to enhance its electrochemical performance to meet the needs of current energy applications. Consequently, investigations are carried out on two niobium based compounds namely niobium tungsten oxide (Nb18W16O93) and niobium-molybdenum oxide (MoNb12O33) nanowires. \nThe nanowires of both materials were fabricated using the electrospinning technique. Firstly, the effect of working parameters on the electrospinning of Nb18W16O93 and MoNb12O33 nanofibers were studied and optimized using central composite design (CCD) based on the response surface methodology (RSM). Experiments were designed to assess the effects of five variables including the applied voltage (V), spinning distance (D), polymer concentration (P), flow rate (F), and addition of NaCl (N) on the resulting diameter of the nanofibers. Prediction models obtained using these variables and verified through analysis of variance (ANOVA) showed that all variables, except flow rate, significantly influenced the nanofibers diameter. These models were used in subsequent experiments to set experimental variables for fabricating Nb18W16O93 and MoNb12O33 nanofibers with reduced diameter. \nTo enhance the electrochemical activities of Nb18W16O93, pristine and nickel-doped (Ni = 1 wt.%, 3 wt.%, 5 wt.%) Nb18W16O93 nanowires were fabricated using the electrospinning technique, followed by annealing. The effect of nickel doping content on the morphology, structure, and electrochemical performance of Nb18W16O93 nanowires was investigated. The findings from the electrochemical experiments reveal that the 3 wt.% nickel-doped nanowires display an impressive capacity retention of 93.1% over 500 cycles at a high current rate of 5 C. Moreover, Ni doping considerably boosted the electronic conductivity in Nb18W16O93 comparison to the pristine nanowires. The CV test results also demonstrate that Ni doping reduces polarization and enhances the lithium-ion diffusion coefficient. \nFurthermore, the possibility of enhancing the electronic conductivity, lithium-ion mobility, and electrochemical kinetics of MoNb12O33 was also explored by fabricating NMO and NMO@H-Ar nanowires (@H-Ar denotes heat treatment under Hydrogen and Argon mixture). The hydrogenation treatment resulted in outstanding electrochemical kinetics, including high reversible specific capacity, high initial coulombic efficiency, excellent long-term cycling stability, and good rate performance. This study concludes that Ni doping and hydrogenation treatment considerably improved the electrochemical activities of Nb18W16O93 and MoNb12O33 nanofibers, which is beneficial for developing new anode materials for LIBs.
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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 ».