Construction of an Array of Photonic Crystal Films for Visual Differentiation of Water/Ethanol Mixtures
Bibliographic record
Abstract
A photonic crystal film (PCF) which consists of a porous layered structure with a highly ordered periodic arrangement of nanopores has been used to differentiate between various mixtures of water and ethanol (EtOH). The refractive index difference between the wall (silica) of the empty nanopore and air which occupies it results in the structural color of the PCF. This color disappears when the nanopores are infiltrated by a liquid with a similar refractive index to silica (or silicon dioxide). The disappearance of the structural color provides a means to construct a colorimetric sensor to differentiate between various water/EtOH mixtures based on their wettability of the nanopores in the PCF. In this study, an array of silica-based PCFs was synthesized on a silicon substrate with a precise control of nanopore properties using the co-assembly/sedimentation method. Using this method, we benefitted from having different PCFs on a single substrate. Chemical coatings, neck angles, and film thicknesses on each PCF were the three factors used to adjust the wettability of the pores. Nanopore wetting by water/EtOH mixtures was studied in a systematic manner based on the three factors, and the findings were used to develop a sensor for visual differentiation of various water/EtOH mixtures. The final developed sensor consisting of an array of six PCFs was able to differentiate between seven different water/EtOH mixtures: W10, W20, W30, W40, W50, W60, and W70, in which W10 means 10% of water in EtOH.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".