Simultaneous electro-generation/polymerization of Cu nanocluster embedded conductive poly(2,2′:5′,2′′-terthiophene) films at micro and macro liquid/liquid interfaces
Bibliographic record
Abstract
Abstract Cu nanoparticles (NPs) have been shown to be excellent electrocatalysts, particularly for CO2 reduction – a critical reaction for sequestering anthropogenic, atmospheric carbon. Herein, the micro interface between two immiscible electrolyte solutions (ITIES) is exploited for the simultaneous electropolymerization of 2,2′:5′,2′′-terthiophene (TT) and reduction of Cu2+ to Cu nanoparticles (NPs) generating a flexible electrocatalytic composite electrode material. TT acts as an electron donor in 1,2-dichloroethane (DCE) through heterogeneous electron transfer across the water|DCE (w|DCE) interface to CuSO4 dissolved in water. The nanocomposite formation process was probed using cyclic voltammetry as well as electrochemical impedance spectroscopy (EIS). CV and EIS data show that the film forms quickly; however, the interfacial reaction is not spontaneous and does not proceed without an applied potential. At high [TT] the heterogeneous electron transfer wave was recorded voltammetrically but not at low [TT]. However, probing the edge of the polarizable potential window was found to be sufficient to initiate electrogeneration/electropolymerization. SEM and TEM were used to image and analyze the final Cu NP/poly-TT composites and it was discovered that there is a concomitant decrease in NP size with increasing [TT]. Preliminary electrocatalysis results at a nanocomposite modified large glassy carbon electrode saw a > 2 × increase in CO2 reduction currents versus an unmodified electrode. These data suggest that this strategy is a promising means of generating electrocatalytic materials for carbon capture. However, films electrosynthesized at a micro and ~ 1 mm ITIES demonstrated poor reusability.
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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".