Noncovalent Functionalization of Graphene Nanoplatelets and Their Applications in Supercapacitors
Bibliographic record
Abstract
We demonstrate a simple noncovalent functionalization technique, which involves graphite exfoliation and subsequent coating of the resulting graphene nanoplatelets (GNPs) with trimellitic anhydride (TMA), using a thermomechanical exfoliation process. TMA adsorbs on the surface of the GNPs, resulting in a reduction of the specific surface area to 312 ± 9 m 2 /g compared to 410 ± 12 m 2 /g for the unmodified GNPs. Detailed imaging, thermogravimetric, and X-ray diffraction analysis showed that the modified GNPs (TMA-GNPs) maintain similar structure to the unmodified GNPs. The presence of functional groups, confirmed by X-ray photoelectron spectroscopy analysis, caused an increase in the surface energy from 45.6 mJ/m 2 for the GNPs to 57.9 mJ/m 2 for TMA-GNPs. The resulting coated TMA-GNPs form stable dispersions in water while maintaining their inherent conductive properties, thus enabling applications, such as the manufacture of conductive films and supercapacitors. As a proof-of-concept, electrodes for supercapacitors are prepared from concentrated aqueous dispersions of the functionalized GNPs. Electrochemical characterization of the supercapacitors using electrochemical impedance spectroscopy, cyclic voltammetry and galvanostatic charge/discharge tests showed a specific capacitance of 22.2 F/cm 3 at a scan rate of 1 mV/s from cyclic voltammetry and 17.3 F/cm 3 at a current density of 1 A/g from galvanostatic charge/discharge tests, with a 90% capacitance retention after 10,000 cycles.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 teacher head, 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".