Melamine-Based, N-Doped Carbon/Reduced Graphene Composite Foam for Li-Ion Batteries and Hybrid Supercapacitors
Bibliographic record
Abstract
Carbon foams, especially graphene foams, has recently received a lot of interest in the field of flexible energy storage due to their excellent electrochemical properties and flexibility. In this work, a compressible melamine-based carbon/reduced graphene oxide foam was synthesized using a simple one-step process. The nitrogen-rich melamine allows the final composite material to be rich in nitrogen, resulting in a higher conductivity and performance. The synergistic combination of nitrogen-doped carbon foam and reduced graphene oxide results in a compressible, free-standing, binder-free electrode (shown in Figure 1) with a capacity of 330 mAh g-1 at 0.1 A that can be used in both Lithium-ion Hybrid Supercapacitors (LIHSs) and Lithium-ion Batteries (LIBs). The composite electrode can also be used as a current collector for other active materials. As a proof of concept, a LIHS was fabricated using the composite foam as a free-standing electrode in both the cathode an anode. The resulting device has an energy density of 40 Wh kg-1 at 1 A that can be maintained for 800 charge and discharge cycles. Figure 1 (a) 3D representation of the cMEGX synthesis procedure, (b) SEM image of neat melamine, (c) SEM image of a representative cMEGX sample, (d) optical image of cMEG800 showing its compressibility Figure 1
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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 | 0.001 | 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".