Synthesis and Electrochemical Study of Graphene Based Nanomaterials for Energy and Environmental Applications
Bibliographic record
Abstract
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.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| 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".