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 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 | 0.001 | 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".