Dip-in Indicators for Visual Differentiation of Fuel Mixtures Based on Wettability of Fluoroalkylchlorosilane-Coated Inverse Opal Films
Bibliographic record
Abstract
We have developed the dip-in indicator based on the inverse opal film (IOF) for visual differentiation of organic liquid mixtures, such as oil/gasoline or ethanol/gasoline fuel mixtures. The IOF consists of a three-dimensional porous structure with a highly ordered periodic arrangement of nanopores. The specularly reflected light at the interface of the nanopores and silica walls contributes to the structural color of the IOF film. This color disappears when the nanopores are infiltrated by a liquid with a similar refractive index to silica. The disappearance of the structural color provides a means to differentiate various liquid fuel mixtures based on their wettability of the nanopores in the IOF-based indicators. For differentiation of various liquid mixtures, we tune the wettability threshold of the indicator in such a way that it is wetted (color disappears) by one liquid but is not wetted by the other (color remains). Although colorimetric differentiation of liquids based on IOF wettability has been reported, differentiation of highly similar liquid mixtures require complicated readout approaches. It is known that the IOF wettability is controlled by multiple surface properties (e.g., oleophobicity) and structural properties (e.g., neck angle and film thickness) of the nanostructure. Therefore, we aim to exploit the combined tuning of these properties for differentiation of fuel mixtures with close compositions. In this study, we have demonstrated that, for the first time, the IOF-based dip-in indicator is able to detect a slight difference in the fuel mixture composition (i.e., 0.4% of oil content). Moreover, the color/no-color differentiation platform is simple, powerful, and easy-to-read. This platform makes the dip-in indicator a promising tool for authentication and determination of fuel composition at the point-of-purchase or point-of-use.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".