Electrocatalytic lignin oxidation for hydrogen and fine chemical co-production using platinized nickel foam in a 3D printed reactor
Bibliographic record
Abstract
Biomass electrooxidation has garnered much attention in recent years, owing to its potential to circumvent greenhouse gas emissions. Substituting the sluggish water oxidation with biomass oxidizable species such as lignin at anode is thermodynamically more favorable, enabling energy efficient hydrogen production and concomitant fine chemicals. The present study shows the organosolv lignin electrooxidation in an additively manufactured 3D printed reactor (3DPR) consisting of platinized nickel foam (PtNF) as anode and cathode and compared with commercial hardware electrolyzer (CHE). The electrolysis of organosolv lignin in 3DPR outperformed CHE by achieving 1.23 times higher current at an applied voltage range from 0 to 2.2 V with a membrane (Nafion 115) interposed between anode and cathode under a continuous flow of lignin feed at the anode. The chronoamperometry study reveals a mixture of diverse aromatic compounds, including vanillic acid, syringic acid, 3,5-dimethoxy-4-hydroxyacetophenone, 2-hydroxyacetophenone, 4-ethycathecol, and 2,6-dimethoxyphenol in anolyte, and sinapic acid and vanillin acetate in catholyte. Thus, realizing renewable biomass electrolysis in the 3DPR is an intriguing strategy for the co-production of hydrogen and fine aromatic chemicals. • Lignin electrooxidized by platinized nickel Foam using as anode and cathode. • Lignin electrooxidation conducted in a 3D printed reactor. • Lignin electrooxidation required lower overvoltage to produce hydrogen. • Fine chemicals generated along with hydrogen upon lignin electrolysis.
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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".