Heteroatom-Doped Carbon Nanomaterials Derived from Black Liquor for Electrochemical Oxygen Reduction Reaction
Bibliographic record
Abstract
Black liquor is hazardous and one of the main byproducts in the pulp and paper industry. Its primary constituent is lignin, a carbon-based molecule serving as a precursor for the synthesis of nanostructured carbon materials. Herein, we have used black liquor as a precursor to synthesize high surface area carbons for use as electrochemical oxygen reduction reaction (ORR) catalysts. The materials were activated by a NaOH treatment and subsequently nitrogen-doped by mixing with dicyandiamide, followed by pyrolysis. Synthesis resulted in catalyst materials that showed high specific surface area (1807 and 1228 m 2 g –1, respectively), high surface nitrogen content (6.7 and 5.1 at. %, respectively), and the inclusion of chromium and sulfur impurities that originated from the black liquor. The black liquor-based catalyst exhibited high ORR activity in alkaline media with a half-wave potential ( E 1/2 ) of 780 mV and an onset potential ( E onset ) of 900 mV versus RHE. The resultant Zn–air battery delivered a high peak power density of 112 mW cm –2 at 171 mA cm –2 and a specific capacity of 633 mAh g –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 | 0.000 | 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".