Synthesis and Electrochemical Study of Three-Dimensional Graphene-Based Nanomaterials for Energy Applications
Bibliographic record
Abstract
Graphene is an attractive soft material for various applications due to its unique and exclusive properties. The processing and preservation of 2D graphene at large scales is challenging due to its inherent propensity for layer restacking. Three-dimensional graphene-based nanomaterials (3D-GNMs) preserve their structures while improving processability along with providing enhanced characteristics, which exhibit some notable advantages over 2D graphene. This feature article presents recent trends in the fabrication and characterization of 3D-GNMs toward the study of their morphologies, structures, functional groups, and chemical compositions using scanning electron microscopy, X-ray diffraction, Raman spectroscopy, Fourier transform infrared spectroscopy, and X-ray photoelectron spectroscopy. Owing to the attractive properties of 3D-GNMs, which include high surface areas, porous structures, improved electrical conductivity, high mechanical strength, and robust structures, they have generated tremendous interest for various applications such as energy storage, sensors, and energy conversion. This article summarizes the most recent advances in electrochemical applications of 3D-GNMs, pertaining to energy storage, where they can serve as supercapacitor electrode materials and energy conversion as oxygen reduction reaction catalysts, along with an outlook.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".