Facile Approach for Synthesizing High-Performance MnO/C Electrodes from Rice Husk
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Pyrolysis is an effective way to convert biomass into biofuel while obtaining highly porous active carbon materials. In this study, a facile approach, involving hydrothermal and pyrolysis steps, is described for preparing hybrid metal oxide nanoparticle-embedded porous hard carbon matrices (MnO/C) from the biowaste rice husk and organometallic precursors. It was found that the pyrolysis/calcination temperature had a strong influence over the microstructure, especially over the porosity, but also over the carbon content and crystallinity of the nanocomposites; hence, the electrical properties can be controlled. Galvanostatic measurements showed that the nanocomposite obtained at 600 °C exhibited the highest charge/discharge capacity and best stability, delivering an initial discharge capacity of 1104 mA·h·g –1 at a current density of 200 mA·g –1, and retaining a value of 830 mA·h·g –1 after 200 cycles, suggesting excellent cycle stability. A discharge capacity of 581 mA·h·g –1 was obtained even at a current density as high as 2400 mA·g –1, demonstrating superb rate capability. This outstanding electrochemical performance, ascribed to high electrochemical activity of the embedded MnO nanoparticles enhanced by electrical conductivity provided through the high surface area of the active porous carbon support, is discussed in relation to the microstructure of the nanocomposite.
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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 | 0.000 | 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".