Electrochemical Reduction of Graphene Oxide on the Gold Surface: Localized Electrochemical Impedance and In Situ Polarization Modulation Infrared Reflection Absorption Spectroscopic Studies
Bibliographic record
Abstract
Graphene oxide (GO) plays an important role in the development of graphene-based nanomaterials and nanocomposites for clean energy, environmental, sensing, and medical applications. In the present study, scanning electrochemical cell microscopy-local electrochemical impedance spectroscopy (SECCM-LEIS) and in situ polarization modulation infrared reflection absorption spectroscopy (PM-IRRAS) were employed to study the reduction of graphene oxide at the gold electrode surface as a function of the applied electrode potential. GO was stable on the gold surface when the electrode potential was higher than −0.4 V and partially reduced to rGO in the potential region between −0.4 and −0.8 V. It was completely reduced to rGO at the potential lower than −0.9 V. The SECCM-LEIS results showed that the conductivity and capacitance of rGO were much higher than those of GO. The PM-IRRAS spectra confirmed the existence of oxygen-containing functional groups in GO. The C═O bonds in GO were reduced to the C–OH and C–O–C bonds in rGO, and the C/O ratio of rGO was slightly increased. During the electrochemical reduction, the amount of the C═C bonds in the aromatic ring was increased, while the amount of the nonaromatic C═C bonds was decreased. The findings in the present study shed light on the mechanism of electrochemical reduction of GO and provide a facile approach for the formation of rGO on metal substrates with controllable oxygen functional groups.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".