Impact of Temperature an Order of Magnitude Larger Than Electrical Potential in Lignin Electrolysis with Nickel
Bibliographic record
Abstract
Abstract Lignin, a major component of plant biomass, is a promising sustainable alternative carbon‐based feedstock to petroleum as a source of valuable aromatic compounds such as vanillin. However, lignin upgrading reactions are poorly understood due to its complex and variable molecular structure. This work focuses on electrocatalytic lignin upgrading, which is efficient and sustainable at moderate temperatures and pressures and does not require stoichiometric reagents. We used a meta‐analysis of published lignin conversion and product yield data to define the operating range, to select the catalyst, and then performed electrocatalytic experiments. We quantified the impact of temperature and electrical potential on the formation rate of valuable products (vanillic acid, acetovanillone, guaiacol, vanillin, and syringaldehyde). We found that increasing temperature increases their formation rate by an order of magnitude more than increasing electrical potential. For example, increasing temperature from 21 to 180 °C increases the vanillin formation rate by +16.5 mg⋅L−1⋅h−1 ±1.7 mg⋅L−1⋅h−1, while increasing electrical potential from 0 to 2 V increases the vanillin formation rate by −0.6 mg⋅L−1⋅h−1 ±1.4 mg⋅L−1⋅h−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 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".