1 Host-guest Chemistry Meets Electrocatalysis: Cucurbit[6]uril on a Au Surface as Hybrid System in CO2 Reduction
Bibliographic record
Abstract
The rational control of forming and stabilizing reaction intermediates to guide specific reaction pathways remains to be a major challenge in electrocatalysis. In this work, we report a surface active-site engineering approach for modulating electrocatalytic CO2 reduction using the macrocycle cucurbit[6]uril (CB[6]). A pristine gold surface functionalized with CB[6] nanocavities was studied as a hybrid organic-inorganic model system that utilizes host-guest chemistry to influence the heterogeneous electrocatalytic reaction. The combination of surface-enhanced infrared absorption (SEIRA) spectroscopy and electrocatalytic experiments in conjunction with theoretical calculations supports capture and reduction of CO2 inside the hydrophobic cavity of CB[6] on the gold surface in aqueous KHCO3 at negative potentials. SEIRA spectroscopic experiments show that the decoration of gold with the supramolecular host CB[6] leads to an increased local CO2 concentration close to the metal interface. Electrocatalytic CO2 reduction on a CB[6]-coated gold electrode indicates differences in the specific interactions between CO2 reduction intermediates within and outside the CB[6] molecular cavity, illustrated by a decrease in current density from CO generation, but almost invariant H2 production compared to unfunctionalized gold. The presented methodology and mechanistic insight can guide future design of molecularly engineered catalytic environments through interfacial host-guest chemistry.
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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".