Graphene Oxide Concentration Effect on the Optoelectronic Properties of ZnO/GO Nanocomposites
Bibliographic record
Abstract
In this work, the effects of graphene oxide (GO) concentrations (1.5 wt.%, 2.5 wt.%, and 5 wt.%) on the structural, morphological, optical, and luminescence properties of zinc oxide nanorods (ZnO NRs)/GO nanocomposites, synthesized by a facile hydrothermal process, were investigated. X-ray diffraction (XRD) patterns of NRs revealed the hexagonal wurtzite structure for all composites with an average coherence length of about 40-60 nm. A scanning electron microscopy (SEM) study confirmed the presence of transparent and wrinkled, dense GO nanosheets among flower-like ZnO nanorods, depending on the GO amounts used in preparation. Raman spectroscopy, Fourier transform infrared spectroscopy (FTIR), ultraviolet-visible (UV-Vis) absorption spectroscopy, and photoluminescence (PL) measurements revealed the impact of GO concentration on the optical and luminescence properties of ZnO NRs/GO nanocomposites. The energy band gap of the ZnO nanorods was independent of GO concentration. Photoluminescence spectra of nanocomposites showed a significant decrease in the intensities in the visible light range and red shifted suggesting a charge transfer process. The nanocomposites' chromaticity coordinates for CIE 1931 color space were estimated to be (0.33, 0.34), close to pure white ones. The obtained results highlight the possibility of using these nanocomposites to achieve good performance and suitability for optoelectronic applications.
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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 | 0.000 |
| 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".