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Enregistrement W2744316279 · doi:10.1149/ma2017-02/4/340

Subeutectic Growth of Carbon-Coated Silicon Nanowires on Carbon Fabric As Self-Supported Electrodes for Flexible Lithium-Ion Batteries

2017· article· en· W2744316279 sur OpenAlexaff
Xiaolei Wang, Ge Li, Xingcheng Xiao, Zhongwei Chen

Notice bibliographique

RevueECS Meeting Abstracts · 2017
Typearticle
Langueen
DomaineEngineering
ThématiqueAdvancements in Battery Materials
Établissements canadiensConcordia UniversityUniversity of Waterloo
Organismes subventionnairesnon disponible
Mots-clésMaterials scienceAnodeElectrolyteLithium (medication)NanowireSiliconElectrodeNanotechnologyChemical vapor depositionCarbon fibersChemical engineeringOptoelectronicsComposite materialChemistry

Résumé

récupéré en direct d'OpenAlex

Developing rechargeable lithium-ion batteries (LIBs) with high energy density, which is of critical importance in energy storage applications such as portable electronics, hybrid electric vehicles (HEVs), and electric vehicles (EVs), requires the development of electrode materials with high capacity. Among the many candidates, silicon (Si) holds great promise and has been extensively studied as anode material owing to its natural abundance and theoretical specific capacity higher than that of graphite (4200 vs 372 mA h g–1) with similar working potential, giving a significantly higher energy density of LIBs. However, Si suffers from a dramatic volume and structure variation during the Li alloying/dealloying process, resulting in the severe pulverization and delamination from current collectors. Moreover, the dynamic formation and decomposition of the solid electrolyte interphase (SEI) layer caused by the side reaction between electrolyte and newly exposed Si surface leads to large irreversible capacities and further causes the rapid performance degradation of the Si-based electrodes. To conquer these critical obstacles, nanostructured Si materials have been widely investigated because they are capable of tolerating extreme changes in volume. Particularly, Si nanowire (NWs) has been considered as the most compelling candidate, since the one-dimensional structure provides not only the shorter lithium-ion diffusion distance due to the narrow diameter combined with long continuous paths for electron transport down their length but also a highly porous architecture, allowing volume variation. The most common approach for the growth of Si NWs involves the medium- or high-pressure chemical vapor deposition (CVD) through the vapor–liquid–solid mechanism, where expensive catalysts (e.g., Au, Ag, and Ga) and hazardous materials (e.g., silane) are often applied. Therefore, advanced technology is highly demanded in the synthesis of high-quality Si NWs for scalable production at low cost. On the other hand, conventional electrode fabrication technology requires significant fractions of binder and conductive agent, which inevitably sacrifice overall energy. Direct deposition of active materials on highly conductive current collectors avoids the use of inert components, resulting in a higher total capacity and a better active material utilization. Moreover, the intimate contact facilitates the electron transport and efficiently improves the electrode integrity as well. Furthermore, the utilization of flexible current collectors enables flexible electrodes, which have been attracting tremendous interest in applications such as wearable electronics and health care devices. So far, there are only a few reports on the fabrication of Si NW-based binder-free and flexible electrodes; however, these flexible electrodes either involve complex synthesis, which is hard for scale-up, or are composed of Si NWs with unsatisfactory quality or nonuniform size. In this context, it is of great importance and challenge as well for effective design and scalable fabrication of high-performance flexible Si NW-based electrodes for practical LIB applications. Herein, we demonstrate self-supported, binder-free, flexible electrodes with long cycling life and controllable high mass loading based on carbon-coated Si NWs grown in situ on highly conductive carbon fabric substrates (c-Si NWs/CF). The carbon-coated Si NWs are synthesized through a nickel catalyzed, bottom-up growth process via a one-pot atmospheric pressure CVD. Ni nanoparticles are first electrodeposited onto a piece of precleaned CF before the Si NWs are grown on the Ni catalyst using SiCl4 as the precursor through a vapor–solid–solid mechanism at a subeutectic temperature of 900 °C (below the eutectic temperature of 993 °C for Ni–Si system in the VLS growth mechanism). A thin carbon layer is coated on the Si NWs using toluene as the carbon source, forming a core–shell structure. Such novel electrodes with a unique three-dimensional architecture possess several characteristics needed for high-performance Si-based electrodes. First, the 1D Si NWs and the porous electrode architecture accommodate the volume change well, while the thin-layer carbon coating effectively confines the Si NWs during cycling. Second, the in-situ growth not only ensures intimate contact of the Si NWs with highly conductive CF, enabling a fast electron transport, but also improves the electrode integrity. Third, high mass loading of carbon-coated Si NWs can be achieved, which holds great potential for next-generation LIBs with high areal capacity and energy density. The high-quality carbon-coated Si nanowires resulted in high reversible specific capacity (∼3500 mA h g–1 at 100 mA g–1), while an exceptionally long cyclability with a capacity retention of ∼66% over 500 cycles at 1.0 A g–1 was achieved. The controllable high mass loading enables an electrode with extremely high areal capacity of ~5.0 mA h cm–2. Such a scalable electrode fabrication technology and the high-performance electrodes hold great promise in future practical applications in high energy density LIBs. Figure 1

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)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
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 Opus0,015
Tête enseignante GPT0,255
Écart entre enseignants0,240 · 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
Publié2017
Routes d'admission1
Résumé présentoui

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