Nanocomposite of Iron Oxide-Reduced Graphene Oxide Applied as High-Performance Anode Materials for Lithium Ion Batteries
Bibliographic record
Abstract
Abstract: Novel nanostructured iron oxide-reduced graphene oxide composites were synthesized by a facile one-step hydrothermal method in an ethylene glycol (EG)–water system. Different phases of iron oxides were detected by adjusting fabrication parameters including the EG/H 2 O ratio, base content and iron ions concentration. Electrochemical propertyof fabricated nanocomposites as anode material was examined in a coin–type cell. The high–rate capacity and cycling stability were found, which is attributed to the improved lithium storage capability due to the application of graphene sheet acting as conductive materials for iron oxide nanoparticles. This study provides a favorable approach for exploring the nanocomposites of metal oxide-graphene anode for lithium ion battery applications. Introduction: Currently,α–Fe 2 O 3 has been considered as a promising candidate for lithium ion batteries due to its much higher theoretical capacity of 1350 mAhg‾ 1 than that of commercial graphite anode materials and its environmental friendly fabrication methods from low–cost resources [1]. In particular, the use of nanomaterials is an effective path to improve rate capabilities of solid state electrodes in batteries attributed to their relatively small diffusion lengths. Furthermore,graphene, with an excellent electronic conductivity, a high theoretical surface area of 2630 m 2 /g and superior mechanical properties, is a significantly promising component for high performance electrode materials [2]. In this study, an anode material of iron oxide nanoparticles (mainly α–Fe 2 O 3 )–reduced graphene oxide for lithium ion battery has been synthesized and reported. Experimental: FeCl 3 ·6H 2 O (1.08 g) and NaOH (0.8 g) was dissolved in EG (30 ml) by ultrasonication for 1 hour.Then 10 ml deionized water and 15 mg graphene oxide was added to the mixture under stirring to get a homogeneous solution. The solution was transferred into a 50 ml teflon–lined stainless steel autoclave, sealed and heated at 200 o C for 10 hours. The product was collected by centrifuging and washed by ethanol and deionized water alternatively for several times, which was followed by drying at 80 o C. Morphologies and phases of synthesized nanocomposites were characterized by scanning electron microscopy (SEM), Raman scattering spectroscopy, X-ray diffraction (XRD), high resolution transmission electron microscopy(TEM). Results: Typical XRD pattern of the products was illustrated in Fig.1 that demonstrated the co–existence of α–Fe 2 O 3 and Fe 3 O 4 . Diffraction peaks can be indexed to either the rhombohedral phase of α-Fe 2 O 3 (JCPDS NO.84-0307) or the cubic phase of Fe 3 O 4 (JCPDS NO.65-3107). The characteristic peak of graphene oxide located at 10.4° was not found, confirming the formation of graphene. Fig. 1 also showed the SEM image of fabricated iron oxide nanoparticles that were uniform with the diameter about 50 nm, indicating XRD pattern in good agreement with SEM results. Raman spectra also confirmed that the typical features of reduced graphene oxide with the presence of D band (1348 cm −1 ) and G band (1598 cm −1 ). References: [1] P. C. Wang, H. P. Ding, Tursun Bark, and C. H. Chen, Electrochimica Acta,52 (2007) 6650–6655 [2] S. Stankovich, D. A. Dikin, G. H. B. Dommett, K.M. Kohlhaas, E. J. Zimney, E. A. Stach, R. D. Piner, S.T. Nguyen, and R.S. Ruoff, Nature, 442 (2006) 282–286
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".