Facile Process and Electrochemical Characterizations of Carbon Nanotube-LiFePO<sub>4</sub> Composite As a Cathode for High Rate Lithium Ion Batteries
Bibliographic record
Abstract
Abstract: LiFePO4 nanoparticles were incorporated with multi-walled carbon nanotubes (CNTs) via a facile one-step at low temperature polyol process. The CNTs were fund to embed into the LiFePO4 particles that form a network to enhance the electrochemical performance of LiFePO4 electrode. Structural morphologies of these CNT-LiFePO4 composites were investigated by X-ray diffraction, scanning electron microscopy and transmission electron microscopy. The electrochemical properties were analyzed by charge/discharge testing and cyclic voltammetry. Primary results showed that the CNT-LiFePO4 composites exhibited enhanced electrochemical performance with a good reversibility and the electronic conductivity. Introduction: Lithium-ion batteries have been considered to play an important role in the electric vehicles (HEV) and plug-in HEV (PHEV) in the near future, which require high power density and high energy density. Thus, cathode materials with high rate capability are in demand [1, 2]. LiFePO4 shows promise as a cathode material for HEVs owing to its high specific capacity and excellent structural stability [3, 4]. However, its poor electronic conductivity and lithium diffusion significantly limit its performance at high rates [5-9]. Therefore, studies on LiFePO4 have focused on improving its rate capability. Coatings of conductive materials [5-10] has been reported to improve the electronic conductivity. Recent efforts have been made to improve rate performance of the LiFePO4 by the synthesized nanocomposites using carbon nanotubes (CNTs) [11]. These nano-carbons improve the high rate capability of LiFePO4, when they are uniformly dispersed in the composite. Herein we report the facile synthesis of CNT-LiFePO4composites with the CNT network providing a conduction path. Experimental: Multi-walled carbon nanotubes (5 wt.% ) were functionalized via the mixed acid method in order to remove the impurities and acquire excellent dispersion in the polyol medium. CNT-LiFePO4 nanocomposites were obtained by using a polyol process as follows: first, the CNTs were added into 100 ml of ethylene glycol and a homogeneous dispersion was achieved by an ultrasonic process. Then, pristine LiFePO4 particles (~50 nm) were directly dissolved into the dispersion by mechanical agitation in a three-neck round flask. Lastly, the mixed dispersion was heated to above its boiling point and maintained for 10 h under stirring and refluxing. After cooling down, the products were separated from the solvent via centrifugation. In order to remove the organic residue impurities, the products were washed with deionized water several times. Finally, the CNT-LiFePO4composite powders were dried at 100 °C for 24 h. Structural morphology were characterized by X-ray diffraction, scanning and transmission electron microscopy (SEM, TEM). Electrochemical performances were investigated by galvanostatic charge-discharge and cyclic voltammetry (CV) over the voltage between 2.0 and 4.0 V using coin cells (CR2032) at a LAND-CT2001A battery-testing system. Results: The morphology of the composites were investigated by scanning electron microscopy (SEM), as showed in Fig.1 that the LiFePO4 nanoparticles are dispersed uniformly with the CNT network. The electrochemical CVs tests were performed in the voltage of 2.0 and 4.0 V at the scanning rate of 0.1 mV s−1. A pair of redox peaks is clearly exhibited in the CNT-LiFePO4 composite cathode, which correspond to the two-phase charge-discharge reaction of the Fe2+/Fe3+ redox couple. The peak curves are very symmetric. The oxidation and reduction peaks appear at around 3.5 and 3.3 V with the potential gap of 0.2 V, which is less than the value of pristine LiFePO4 cathode as reported, and suggested that electrode reaction reversibility and the electronic conductivity of these composites are greatly enhanced owing to the incorporation of CNTs. Acknowledgements: Financial supports from University of Waterloo President’s Award, Natural Sciences and Engineering Research Council of Canada (NSERC) and Waterloo Institute for Nanotechnology (WIN) are greatly appreciated. References: [1] J. W. Fergus, J. Power Sources, 195 (2010) 939. [2] P. Gibot, M. Casas-Cabanas, L. Laffont, S. Levasseur, P. Carlach, S. Hamelet, J.-M. Tarascon, C. Masquelier, Nat. Mater., 7 (2008) 741. [3] A. K. Padhi, K.S. Nanjundaswamy, J.B. Goodenough, J. Electrochem. Soc., 144 (1997) 1188. [4] S. F. Yang, P.Y. Zavalij, M. S.Whittingham, Electrochem. Commun. 3 (2001) 505. [5] X. L. Yang, F. Mou, G. Peng, Z. X. Dai, Z.Y. Wen, J. Power Sources, 204 (2012) 182. [6] M. Konarova, I. Taniguchi, J. Power Sources, 195 (2010) 3661. [7] X. M. Lou, Y.X. Zhang, J. Mater. Chem. 21 (2011) 4156. [8] F. Q. Cheng, W. Wan, Z. Tan, Y.Y. Huang, H.H. Zhou, X.X. Zhang, Electrochim. Acta, 56 (2011) 2999. [9] Y. H. Huang, H.B. Ren, S.G. Yin, Y.H. Wang, Z.H. Peng, Y.H. Zhou, J. Power Sources, 195 (2010) 610. [10] Y.H. Huang, J.B. Goodenough, Chem. Mater., 20 (2008) 7237. [11] T. Muraliganth, A.V. Murugan, A. Manthiram, J. Mater. Chem., 18 (2008) 5661. Figure 1
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".