Electrocatalytic Reduction of Lignin Model Compounds in a Stirred Slurry Reactor: Mild Approach for Synthesis of Renewable Chemicals
Bibliographic record
Abstract
Electrocatalytic hydrogenation-hydrogenolysis (ECH) is a promising approach for synthesis of bio-based chemicals. The advantages of this process over the classic thermocatalytic routes are mainly attributed to the mild operating conditions with in-situ H2 generation and the feasibility of product selectivity control by synergistic interactions between the electrode potential, current density, and temperature. In this work, a mild electrosynthesis of value-added chemicals from lignin model compounds (e.g., phenol, guaiacol, and creosol) is presented using a stirred slurry electrochemical reactor (SSER). Cyclohexanol and cyclohexanone are among the main target products, which represent important chemicals for the industrial synthesis of Nylon polymers. The effects of different electrolyte pairs, electrolyte compositions (aqueous and organic mixtures), and electrocatalysts (Pt/C, Ru/C, and Pd/C) were investigated under benign reaction conditions (1 atm, ~60 oC). The primary source of protons for ECH was identified by pairing the diverse electrolytes (H2SO4, NaCl, and NaOH), showing the importance of acidic anolyte for an effective ECH. Methanesulfonic acid was also successfully employed in the ECH of guaiacol with low Pt/C loading, resulting in high guaiacol conversion (~90%) and cyclohexanol selectivity (~50%) at high Faradaic efficiency (>70%) and at low operating temperatures (35–40 oC). The electrocatalytic activity is strongly dependent on the adsorbed hydrogen radical surface coverage, which in turn is a complex function of active sites, electrolyte (pH and composition), and electrode potential. By pairing neutral catholyte (NaCl) and acid anolyte (H2SO4), anodic protons transported through the cation exchange membrane may be effectively utilized for ECH reactions. Pt/C shows superior activity in the acid-acid pairs, while the activity of Ru/C and Pd/C were significantly improved in the neutral-acid pairs. The most obvious example was found in the guaiacol ECH, where Ru/C, which was least active in the acid-acid pair, resulted in in significant conversion (>70%) and Faradaic efficiency (~76%) in the neutral-acid pair after 4 h. Overall, this work demonstrates that the efficient reductive upgrading of lignin monomers can be achieved under mild conditions via electrocatalysis in a stirred slurry electrochemical reactor at high cathode superficial current densities (i.e. above 100 mA cm-2). Figure 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 | 0.001 |
| 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".