Dioctylfluorene-Based Ionomers with Benzimidazolium Pendants Enhance the CO2 Reduction Reaction on Copper
Bibliographic record
Abstract
The use of the CO2 reduction reaction (CO2RR) to form renewable fuels and feedstocks is a promising pathway to reduce carbon emissions. The use of ionomers in CO2 electrolyzers has become an essential component as an immobile electrolyte. Yet, these devices still suffer from poor product selectivity and energy efficiency. Herein, we have developed a series of ionomers containing a backbone with dioctylfluorene (FLU) units as well as a pendant, co-catalytic benzimidazolium cationic group. The use of FLU serves to introduce alkyl chains as hydrophobic moieties and to prevent ionomer adsorption to the catalyst surface. These ionomers allowed us to investigate the trade-off effect between the increasing FLU content and decreasing cationic group content in enhancing CO2RR. Applying the ionomers as Cu-catalyst binders in membrane electrode assembly electrolyzers revealed that FLU could suppress H2 production and increase CO2RR to CO and C2H4. Optimization of the backbone showed that an intermediate FLU content (PFBB-50) led to the highest performance, achieving a C2H4 Faradaic Efficiency of 42% at 3.07 V (jC2H4 = 126 mA/cm2). Mechanistic studies, including molecular dynamics simulations, electrochemical surface area (ECSA), and hydrophobicity measurements, revealed that increasing the alkyl chain coverage using FLU had decreased the ECSA, yet still led to improved performance resulting from the increased hydrophobicity and a high local CO2 concentration. In situ Raman spectroscopy experimentally supported these findings through the appearance of a high Cu-*CO band in PFBB-100, which had not appeared in other samples. Overall, these materials present an important finding for the rational design of ionomers that act as catalyst binders for enhancing CO2RR.
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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) | 0.001 | 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".