A Comparative Investigation of Chemically Reduced Graphene Oxide Thin Films Deposited via Spray Pyrolysis
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide We present a comparative investigation between thin films of graphene oxide (GO) and chemically reduced graphene oxide (rGO) deposited onto glass substrates via spray pyrolysis. Two reduction techniques are investigated: (1) the exposition of a sprayed layer of GO to vapors of hydrazine hydrate to produce rGO V and (2) the addition of liquid hydrazine hydrate to a suspended GO solution, which is then sprayed onto a substrate to produce rGO L . Three different spectroscopy techniques, Raman, Fourier transform infrared, and UV–Vis–NIR, show that the two reduced samples have less lattice disorder in comparison to GO, with rGO L having the highest degree of reduction. Interestingly, topography characterization by atomic force microscopy reveals a morphological change occurring during the exposure to hydrazine hydrate vapors, resulting in a thickness of 110 nm for the rGO V film, which is a factor of 16 larger than rGO L and GO. Finally, I – V measurements show a significant decrease of the GO’s resistivity after the reduction process, where rGO L features a resistivity 90 times lower than rGO V, confirming that rGO L has the highest degree of reduction. Our results indicate that the reduction process for rGO V is susceptible to introducing intercalated water molecules in the material while the fabrication technique for rGO L is a suitable route to obtain a material with minimal lattice disorder and properties approaching those of graphene.
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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) | 0.002 | 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".