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Enregistrement W2566879015 · doi:10.1149/ma2014-02/6/499

Nanocomposites of TiO<sub>2</sub> Nanoparticles-Graphene with High-Rate Performance for Li-Ion Battery

2014· article· en· W2566879015 sur OpenAlexaffabout
Xiangcheng Sun, Edward Hu, Yuefei Zhang, Min He, Lin Gu, Bo Cui

Notice bibliographique

RevueECS Meeting Abstracts · 2014
Typearticle
Langueen
DomaineEngineering
ThématiqueAdvancements in Battery Materials
Établissements canadiensUniversity of Waterloo
Organismes subventionnairesnon disponible
Mots-clésMaterials scienceGrapheneAnataseNanocompositeAnodeNanotechnologyLithium (medication)Chemical engineeringOxideNanoparticleLithium-ion batteryBattery (electricity)ElectrodePhotocatalysisChemistry

Résumé

récupéré en direct d'OpenAlex

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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,001
Score d'incertitude au seuil0,003

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0010,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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.

Tête enseignante Opus0,008
Tête enseignante GPT0,199
Écart entre enseignants0,192 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2014
Routes d'admission2
Résumé présentoui

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