Kraft Lignin Electro-Oxidation under Ambient Temperature and Pressure
Bibliographic record
Abstract
Lignin is the largest natural source of aromatic chemicals. Due to its complex polymeric structure, Kraft lignin is under-utilized and usually combusted for heat generation, thus resulting in CO2 emissions in the Kraft process. To valorize lignin with renewable electricity and to convert it into value-added aromatic chemicals, efficient electrochemical methods need to be discovered, based not only on its apparent yield of building block chemicals but also on its energy efficiency. In this study, the electro-oxidative performance of six different metals was systematically evaluated. The results showed that the synthesized Ni-based catalyst can increase the vanillin and vanillic acid apparent yield by 50–60% compared to when Ni-based catalyst is absent. We also found that the oxygen evolution reaction (OER) is more than a competing reaction since the presence of oxygen synergistically aids oxidation of the lignin to increase aromatic chemical production by 63% compared to the sum of vanillin generation from both voltage-only and oxygen-only scenarios. With the novel proposed notion of charge efficiency, we showed that utilizing a thinner layer of Ni catalyst balances the OER and the oxidative reaction of lignin, thus improving the charge efficiency for vanillin by 22%
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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".