Synthesis and Electrochemical Study of Graphene Based Nanomaterials for Energy and Environmental Applications
Notice bibliographique
Résumé
Graphene based nanomaterials have been demonstrated in advanced energy and environmental applications [1, 2]. But their wide utilization in practical applications is strongly hindered due to the lack of simple, cost effective, and environmentally compatible synthetic methods for the mass production of graphene. To date, mechanical, solution, and chemical based approaches have been extensively explored in the synthesis of graphene; however, each approach has its limitations, particularly in terms of scalability and the characteristics of the resulting graphene. In addition, as for pristine graphene, inherent restacking issues have hampered the use of these nanomaterials in electrochemical applications. Novel properties may be achieved through the introduction of various functional groups and dopants into the graphene, or via the meticulous design of 3D superstructures [3, 4]. For these characteristics and applications, graphene oxide is a promising intermediate for the preparation of graphene based nanomaterials in bulk. In this report, we present a facile one pot synthesis strategy to produce interconnected reduced graphene oxide (IC-rGO) and fluorinated graphene oxides (F-GO). The graphene-based nanomaterials were tested for electrochemical energy storage and water remediation applications. Unique 3D IC-rGO was synthesized using a facile one pot synthesis process that we refer to as Streamlined Hummers Method. The 3D hierarchical structure was advantageous in overcoming restacking issues while improving the heterogeneous electron transfer kinetics of the 2D materials. Unlike the conventional procedure that involved cross-linkers and multiple steps to produce 3D graphene structures, we introduced simple one pot approach to attain stable 3D interconnected reduced graphene oxide. In this approach, the interconnections were enabled though the inherent oxygen functional groups of the graphene oxide. These functionalities were confirmed using Infrared and X-ray photoelectron spectroscopy. Furthermore, the synthesized 3D IC-rGO were characterized using X-ray diffraction, scanning electron microscopy, and transmission electron microscopy. The fabricated 3D IC-rGO was tested as an electrode material for supercapacitor applications. The 3D IC-rGO demonstrated a stable 3D interconnected morphology that enhanced facile ion transport and minimized interlayer resistance. The IC-rGO showed an enhanced specific capacitance (212 F/g at 1.0 A/g) over conventional thermally reduced graphene oxide (93 F/g at 1.0 A/g), with excellent cyclic stability over 5000 cycles. A one-pot synthesis method was also developed for F-doped graphene oxide (F-GO) with enhanced electrochemical activity through modifications to the Improved Hummers Method. Here, in contrast to the conventional technique, we achieved functionalization and oxidation in a single step. The F-GO exhibited a wider interlayer distance and higher defect density than did GO. Approximately 1.14 at.% F semi-ionically doped onto a few layered graphene oxides, which was confirmed by X-ray photoelectron spectroscopy. F-doping was expedient in improving the electrochemical activity of GO. For the first time, F-GO was demonstrated to have the capacity for heavy metal ion sensing. F-GO demonstrated the ability to simultaneously detect ultralow concentrations of heavy metal pollutants, such as Cd, Pb, Cu, and Hg with sensitivities of 3.64, 6.05, 3.64, and 4.24 μA μM− 1, respectively. Further, it exhibited improved double-layer capacitance, in contrast to its non-doped counterpart. References [1] M. Govindhan, B. Mao, A. Chen, Nanoscale 8 (2016) 1485–1492. [2] B.-R. Adhikari, M. Govindhan, A. Chen, Sensors 15 (2015) 22490–22508. [3] B. Sidhureddy, A. R. Thiruppathi, A. Chen, Chem. Commun. 53 (2017) 7828–7831 [4] A. R. Thiruppathi, B. Sidhureddy, W. Keeler, A. Chen, Electrochem. Commun. 76 (2017) 42–46.
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».