Comparison of Au Nanoparticle/Poly(9-vinylcarbazole) Thin-Film Electrogeneration at 3 Distinct Liquid/Liquid Interfaces: Water/1,2-Dichloroethane, /α,α,α-Trifluorotoluene, Or/Ionic Liquid
Bibliographic record
Abstract
Metal nanoparticle (NP) incorporated conductive polymer films are attractive for their mechanical stability for biomedical applications and as heterogeneous electrocatalysis materials. Novel approaches to generate these materials with tunable properties are still being sought. Herein, the interface between two immiscible electrolyte solutions (ITIES) has been employed as a molecularly sharp and reproducible platform for simultaneous Au NP and poly(9-vinylcarbazole) generation. Three interfaces have been compared, including between water|1,2-dichloroethane (w|DCE), water|α,α,α-trifluorotoluene (w|TFT), and water|ionic liquid (w|IL). In this case the IL was P 8888 TB (tetraoctylphosphonium tetrakis(pentafluorophenyl)borate). 9-Vinylcarbazole (VC) can polymerize via two routes, either propagating through the vinyl substituent or the aryl rings. The former gives rise to a white semiconducting polymer with a wide bandgap, while the latter produces a green, conducting polymer. External potential control through voltammetric cycling was found to generate the film more rapidly favoring heterogeneous electron transfer with formation of the green poly(VC) variant at the ITIES. This was a free-standing film that could be easily removed from the interface. In the absence of external control, white polymer crystals formed within the oil phase spontaneously likely via AuCl 4 – w → o transfer followed by a homogeneous electron transfer reaction mechanism. Scanning electrochemical microscopy probe approach curve experiments were used to quantify the electroactivity of the film and are complemented by direct conductivity measurements.
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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".