Influence of Nitrogen Doping and Pre‐Carbonization on the Performance of Lignin‐Derived Activated Carbon for Supercapacitor Applications
Bibliographic record
Abstract
Abstract Nanostructured carbons are widely used as active materials for supercapacitor applications owing to their high specific surface area and electrical conductivity. Biomass waste‐derived materials can be sustainably produced. In this work, highly porous activated carbon was prepared from lignin waste for supercapacitor applications, and the effects of nitrogen doping and a pre‐carbonization treatment on the final performance were investigated. Particularly, activated carbonized lignin (ACL) and activated lignin (AL) samples were prepared with or without a pre‐carbonization step, respectively, and with or without nitrogen doping. Nitrogen doping of the samples was found to decrease the capacitance owing to the loss of critical oxygen‐containing functional groups, which provide pseudocapacitance. Meanwhile, the use of a pre‐carbonization treatment greatly improved the surface area and capacitance of the materials. Sorptometry analysis indicated that ACL and AL have high specific surface areas of 3174 and 2289 m 2 g −1 , respectively. ACL achieved a specific capacitance of 306.4 F g −1 and 292.1 F g −1 in 1 mol L −1 KOH and H 2 SO 4 , respectively. Furthermore, the contribution of a pre‐carbonization treatment to improve the surface area and maintain the presence of oxygen‐containing functional groups was identified as beneficial towards improving the pseudocapacitance properties of the porous carbon materials.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".