Nanocomposites of TiO<sub>2</sub> Nanoparticles-Graphene with High-Rate Performance for Li-Ion Battery
Bibliographic record
Abstract
Abstract: A simple and efficient method is developed to synthesize the nanocomposite of anatase TiO2 and reduced graphene oxide as anode material for Li-ion battery applications. The method involves one-step hydrothermal treatment without any surfactant or high-temperature calcinations. Structure analyse demonstrated that nano-sized anatase TiO2 particles were well dispersed in reduced graphene oxide nano-sheets. These graphene-TiO2 hybrid nanocomposites were electrochemically investigated in the coin-type cells versus metallic lithium, and the lithium storage performance showed an enhanced rate capabilities and cycling stability at different charge/discharge rates. These improved electrochemical performance can be mainly attributed to the fact that conductive graphene nano-sheets attached on nanosized TiO2 particles provide high electrical conductivity. Introduction : TiO2 has been regarded as a promising anode material for lithium-ion batteries due to its environmental benignity, low cost, and good safety [1–3]. However, its practical capacity and high-rate capability are limited due to the blow Li-ion diffusivity and electronic conductivity during reversible Li-ion insertion/extraction process [4]. In order to improve the electrochemical performance of TiO2 materials, nanotechnology has been explored to provide increased reaction active sites and short diffusion lengths for both electron and Li-ion transport [5–6]. A variety of approaches have been developed to increase the electronic conductivity of the TiO2, such as adding conductive agents [7] and using conductive coating [8]. Graphene has been regarded as an ideal carbon nanostructure to improve the rate capability of TiO2 owing to its superior electronic conductivity and large surface area. It is found that TiO2-graphene nanocomposite exhibited a high capacity and excellent rate capability in the enlarged potential window of 0.01–3.0 V due to the graphene not only as a conductive agent but also as a lithium storage material [9]. Herein, the nanocomposites of anatase TiO2 nanoparticles and reduced graphene oxide were facilely synthesized, the electrochemical performance of the obtained nanocomposite was investigated as an anode material. Experimental : The nanocomposites of anatase TiO2 nanoparticles-graphene oxides were synthesized by one-step hydrothermal method. Briefly, 25 mg of graphene oxide was firstly added to 75 mL deionized water, then 5 m L of 1.5M sodium hydroxide solution was added to obtain a colloidal solution that was sonicated for 30 min. Such a colloidal solution was subsequently mixed with 50 mg commercial TiO2 nano-powders (ca.25 nm diameter) by high-speed stirring for1 h. The resulting solution was put into an autoclave and heated at 180°C for 10 h. When the reduction reaction was finished, the as-synthesized TiO2-graphene composites were isolated by centrifugation, washed with pure water and ethanol several times, and dried at 80°C for 2h. The structure and morphology were characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM) and transmission electron microscopy (TEM). The electrochemical performances of galvanostatic charge-discharge and cyclic voltammetry (CV) were investigated using coin cells (CR2032) at a LAND-CT2001A battery-testing system. Results : The morphology of the nanocomposites were investigated by scanning electron microscopy (SEM). SEM images in Fig.1 showed that the TiO2 nanoparticles are dispersed uniformly in the graphene oxide with relatively low amounts load. Raman spectra in Fig. 2 shows the typical features of reduced graphene oxide with the presence of D band located at 1340 cm−1 and G band at 1581 cm−1. In addition, the Raman lines for Eg, B1g, A1g, and B1g modes of TiO2anatase phase were also observed. Acknowledgement : Financial supports from the President’s Award of University of Waterloo and Natural Sciences and Engineering Research Council of Canada (NSERC) and Waterloo Institute for Nanotechnology (WIN) are greatly appreciated. References: [1] Z. Yang, D. Choi, S. Kerisit, K.M. Rosso, D. Wang, J. Zhang, G. Graff, J. Liu, J. Power Sources 192 (2009) 588. [2] P. Kubiak, T. Fröschl, N. Hüsing, U. Hörmann, U. Kaiser, R. Schiller, C.K. Weiss, K. Landfester, M. Wohlfahrt-Mehrens, Small 7 (2011) 1690. [3] J. S. Chen, X.W. Lou, Electrochemistry Communications 11 (2009) 2332. [4] S. Bach, J. P. Pereira-Ramos, P. Willman, Electrochimica Acta 55 (2010) 4952. [5] Y. H. Jin, S. H. Lee, H.W. Shim, K.H. Ko, D.W. Kim, Electrochimica Acta 55 (2010) 7315. [6] F. Wu, Z. Wang, X. Li, H. Guo, J. Materials Chemistry 21 (2011) 12675. [7] Y. Wang, T. Chen, Q. Mu, J. Materials Chemistry 21 (2011) 6006. [8] J. S. Chen, H. Liu, S. Z. Qiao, X.W. Lou, J. Materials Chemistry 21 (2011) 5687. [9] D.D. Cai, P.C. Lian, X.F. Zhu, S.Z. Liang, W.S. Yang, H.H. Wang, Electrochimica Acta, 74 (2012) 65.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".