Air-Stable, Self-Bleaching Electrochromic Device Based on Viologen- and Ferrocene-Containing Triflimide Redox Ionic Liquids
Bibliographic record
Abstract
We demonstrate an electrochromic device with self-bleaching ability that uses ethyl viologen- ([EV] 2+ ) and ferrocene-based redox ionic liquids ([FcNTf] − ) as the electroactive species. These electroactive compounds are insensitive to atmospheric O 2 and H 2 O in both their oxidized and reduced states once dissolved in a typical ionic liquid electrolyte ([BMIm][NTf 2 ]), allowing for the device to be assembled outside a glovebox without any encapsulation. This device could generate a deep blue color by the application of a 2.0 V potential between two fluorine-doped tin oxide (FTO) substrates to oxidize the ferrocenyl centers to [FcNTf] 0 while reducing viologen to [EV] +• . Self-bleaching occurs at OCP as [EV] +• and [FcNTf] 0 undergo homogeneous electron transfer in the electrolyte. The mass transport of ethyl viologen and ferrocenylsulfonyl(trifluoromethylsulfonyl)imide ([FcNTf] − ) anion was evaluated by double potential step chronoamperometry to study the impact of the diffusion coefficient on the self-bleaching mechanism. The electrochromic device demonstrated here shows a contrast Δ T (610 nm) around 40% at 2.0 V as colored cell voltage, a switching time in the order of few seconds for coloration and bleaching, coloration efficiency of 105.4 to 146.2 cm 2 C 1– at 610 nm, and very high stability (94.8% Δ T after 1000 cycles) despite the presence of O 2 and H 2 O in the electrolyte.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".