Fabrication Characteristics and Performance Enhancement of Nb18W16O93 and MoNb12O33 Nanowires for Lithium-Ion Batteries Application
Bibliographic record
Abstract
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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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".