Production and Characterization of Metal Oxide Loaded Reduced Graphene Oxide Nanocomposites
Bibliographic record
Abstract
Graphene-based nanocomposite structures have recently attracted considerable attention as advanced materials due to excellent properties of graphene.Graphene plays an important role as a conducting flexible substrate to host active nanomaterials especially for energy applications with its two-dimensional carbon structure [1].Outstanding materials produced with reduced graphene oxide (rGO) by loading metal oxide nanoparticles are especially used as electrodes for Liion batteries.As known, Li-ion batteries are one of the most important energy storage devices due to its high energy density, high power density, long cycle life and environmental friendliness [2].Compared to other battery types, Li-ion batteries are currently used in many devices such as portable electronics, laptop computers and cellular phones due to their higher energy density [3].SiO2 has been considered as one of the promising materials thanks to its low discharge potentials, abundancy and low cost [4].Also, ZnO is a promising anode material for lithium ion batteries due to its high theoretical capacity, which is nearly three times that of the currently used graphite anode [5].In this work, for Li-ion batteries, ZnO-SiO2-reduced graphene oxide (rGO) nanocomposite was produced as anode material.Firstly, graphite oxide was produced from graphite via Hummer's method.Then, SiO2 and ZnO nanoparticles were added to aqueous graphite oxide suspension and ultrasonicated for 1 hour.Afterwards, via vacuum filtration, this suspension was filtrated, and reduction process was applied in hydrazine solution.Consequently, free-standing ZnO-SiO2-rGO nanocomposite papers were obtained.Produced nanocomposites were characterized by field emission scanning electron microscopy (FESEM), energy dispersive X-ray spectrometer (EDS) and X-ray diffraction (XRD) analyses.Optical properties of ZnO-SiO2-rGO nanocomposites were investigated via Fourier transform infrared spectroscopy (FT-IR).
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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 | 0.001 | 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) | 0.001 | 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".