Designing 3D Ternary Hybrid Composites Composed of Graphene, Biochar and Manganese Dioxide as High-Performance Supercapacitor Electrodes
Bibliographic record
Abstract
Biochar derived from waste biomass has proven to be an encouraging novel electrode material in supercapacitors. In this work, luffa sponge-derived activated carbon with a special structure is produced through carbonization and KOH activation. The reduced graphene oxide (rGO) and manganese dioxide (MnO2) are in-situ synthesized on luffa-activated carbon (LAC) to improve the supercapacitive behavior. The structure and morphology of LAC, LAC-rGO and LAC-rGO-MnO2 are characterized by the employment of X-ray photoelectron spectroscopy (XPS), X-ray diffraction (XRD), BET analysis, Raman spectroscopy and scanning electron microscopy (SEM). The electrochemical performance of electrodes is performed in two and three-electrode systems. In the asymmetrical two-electrode system, the LAC-rGO-MnO2//Co3O4-rGO device shows high specific capacitance (SC), high-rate capability and excellent cycle reversibly in a wide potential window of 0–1.8 V. The maximum specific capacitance (SC) of the asymmetric device is 586 F g−1 at a scan rate of 2 mV s−1. More importantly, the LAC-rGO-MnO2//Co3O4-rGO device exhibits a specific energy of 31.4 W h kg−1 at a specific power of 400 W kg−1. Overall, the synergistic effect between the ternary structures of microporous LAC, rGO sheets and MnO2 nanoparticles leads to the introduction of high-performance hierarchical supercapacitor electrodes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".