Innovative approach for the synthesis of graphene/MnO<sub>2</sub> nanocomposites and their electrochemical behavior
Bibliographic record
Abstract
Abstract Graphene‐manganese dioxide composites were prepared using a simple one step method consisting of the electrochemical exfoliation of graphite in the presence of KMnO4 in 0.1 м H2SO4 at low temperature. In these conditions, the freshly exfoliated graphene sheets (EG) spontaneously reduce the permanganate ions and EG sheets decorated with MnO2 nanoparticles are obtained. This was confirmed by scanning electron microscopy coupled with energy dispersive X‐ray spectroscopy, transmission electron microscopy, X‐ray diffraction, Raman, and X‐ray photoelectron spectroscopies and thermogravimetric analysis. The electrochemical properties of EG@MnO2 material were investigated and discussed. Electrodes based on the composite material exhibited enhanced capacitive performances compared to those made from pure graphene sheets. This was attributed to the synergic effect between the two components (graphene sheets and manganese dioxide nanoparticles) and to a larger porosity of the EG@MnO2 electrode compared to the EG electrode. Additionally, over 91.6% of original capacitance was retained after 4000 cycles, indicating a very good cycling capability of the composite material. Remarkably, the composite electrode showed promising properties in aqueous medium by exhibiting a large and stable operating voltage up 2 V. The results presented in this article could serve as a guide for improving the energy density of supercapacitors in aqueous media.
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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".