Hydrophobic molecular assembly at the gas-liquid-solid interface drives highly selective CO2 electromethanation
Bibliographic record
Abstract
The modularity of molecular catalysts enables the tuning of both active site and peripheral units to maximize functionality, thus rendering them as ideal model systems to explore fundamental concepts in catalysis. Hydrophobicity is often regarded as an undesirable aspect that hinders their dissolution in aqueous electrolytes. In contrast, we modified established Co terpyridine catalysts with hydrophobic perfluorinated alkyl side chains and took advantage of their hydrophobic character by utilizing them not as dissolved species in an aqueous electrolyte but at the gas-liquid-solid interfaces on a gas diffusion electrode (GDE) applied towards the electrochemical reduction of CO2. We found that the self-assembly of these perfluorinated units on the GDE surface results in a catalytic system selective for CH4 production, whereas every other Co terpyridine catalyst reported before was only selective for CO or formate. An array of mechanistic and operando spectroscopic investigations suggests a mechanism in which the pyridine units function as proton shuttles that deliver protons to the dynamic hydrophobic pocket in which CO2 reduction takes place. Finally, optimizing the system by integrating fluorinated carbon nanotubes as a hydrophobic conductive scaffold leads to a Faradaic efficiency for CH4 production above 80% at rates above 10 mA/cm2, thus far unprecedented for a molecular electrocatalytic system.
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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".