Lignin Electrolysis at Room Temperature on Nickel Foam for Hydrogen Generation: Performance Evaluation and Effect of Flow Rate
Bibliographic record
Abstract
Water electrolysis is a thermodynamically energy-intensive process. One approach employed to make water electrolysis kinetically favorable is replacing the oxygen evolution reaction (OER) at the anode by facile electrooxidation of biomass-feedstocks such as ethanol, methanol, glycerol, and lignin due to the presence of readily oxidizable functional groups. In this work, we report a simplistic approach for hydrogen generation by lignin electrolysis, utilizing a low-cost nickel foam as both anode and cathode sandwiched with hydroxide ion (OH-) exchange membrane in a 3D printed reactor. The performance of the lignin electrolysis was analyzed under various flow rates of anolyte (lignin)/catholyte (KOH) in the anode and cathode chambers. The lignin electrolysis outcompetes traditional water electrolysis by achieving higher current density in the applied voltage range from 0 to 2.5 V at room temperature. The charge transfer resistance for the lignin electrolysis is lower than that of the water electrolysis characterized by impedance spectroscopy. The enhanced current density from the lignin electrolysis at low overvoltage has been presumed from the oxidation of reactive functional groups present in the lignin, facilitating faster electron transfer. Moreover, the hydrogen production rate calculated from the chronoamperometry test of the lignin electrolysis is 2.7 times higher than that of water electrolysis. Thus, the electrochemical oxidation of lignin can potentially lower the capital cost of renewable hydrogen production.
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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 | 0.000 | 0.001 |
| 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".