Mild Synthesis of Cyclohexanol and Cyclohexanone Via Electrocatalytic Reduction of Phenols Using Dispersed Metal Catalysts
Bibliographic record
Abstract
Cyclohexanol and cyclohexanone are industrially important chemicals for the synthesis of Nylon polymers. On the industrial scale, they are produced by thermocatalytic processes, either phenol hydrogenation (140–170 oC, 1 atm) or cyclohexane oxidation (140–180 oC, 0.8–2 MPa), which require high temperatures and external gas supply. This work demonstrates that electrosynthesis of cyclohexanol and cyclohexanone can be done at mild conditions using diverse aqueous electrolytes and dispersed metal catalysts (Pt/C, Ru/C, and Pd/C). Electrocatalytic hydrogenation (ECH) of lignin-derived phenols (e.g., guaiacol and phenol) is performed in a stirred slurry H-cell under potentiostatic or galvanostatic control. Dilute mineral acid (H2SO4, HClO4), organic acid (CH4SO3), and inorganic salt (NaCl) worked efficiently as the electrolyte solutions. In the ECH of guaiacol at low temperatures (35–40 oC) and low Pt/C loading, high guaiacol conversions (83–96%) and Faradaic efficiencies (40–70%) were achieved with significant cyclohexanol selectivities (45–53%) after 4 h reactions. In the ECH of phenol, full conversion to cyclohexanol was achieved after 2 h with high current efficiency (90%). Remarkably, by pairing the NaCl catholyte and H2SO4 anolyte, the activity of Ru/C and Pd/C can be dramatically improved, showing the importance of electrocatalyst and electrolyte synergy in the ECH of phenolic compounds. The stirred slurry catalyst allows the cell to operate at the industrially relevant current densities (in this work, between 100–300 mA cm-2). Dispersion of the negatively charged catalyst particles in the solution also facilitates the mass transfer between the organic molecules and chemisorbed hydrogens through physical collisions during the reaction, thereby improving the ECH efficiency. Mild electrocatalytic reduction of phenolic compounds represents a promising route for selective and efficient production of renewable chemicals from biomass.
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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 | 0.000 |
| 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".