Optimized Colorimetric Photonic‐Crystal Humidity Sensor Fabricated Using Glancing Angle Deposition
Bibliographic record
Abstract
Abstract Colorimetric sensing, where environmental changes are transduced into visual color changes, provides an intuitively simple yet powerful detection mechanism that is well‐suited to the realization of low‐cost and low‐power sensors. A new approach in colorimetric sensing exploits the structural colour of photonic crystals (PCs) to create new color‐changing materials, however much work is still required to simultaneously achieve optimized sensor response and low‐cost, scalable nanofabrication. This work responds to these challenges by designing, fabricating and evaluating a mesoporous PC sensor optimized to exhibit as large as possible color‐shift in response to small changes in relative humidity (RH). A novel design optimization is achieved by employing a colorimetric framework that translates simulated/measured spectral quantities into numeric color values directly related to color perception. The sensor design is then realized using a mesoporous TiO2 PC, fabricated using glancing angle deposition (GLAD). The GLAD technique is a bottom‐up, single‐step nanofabrication method providing the nanoscale precision required to successfully realize the optimized PC design. The PC sensor is shown to be highly sensitive and stable: the PC structural‐color changes visibly due to RH changes smaller than 1%, and the response is stable over hundreds of hours of sensor operation. Additionally, measurements and simulations are used to reveal the important link between the PC optical modes, pore geometry, and sensor response which will be useful in future PC sensor experiments. The combination of bottom‐up nanofabrication with visible color‐based sensing, coupled with the useful design methodology, will lead to further developments in low‐cost, widely deployable optical sensors.
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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 | 0.001 | 0.000 |
| 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".