Validating 3D Photonic Crystals for Structural Health Monitoring
Bibliographic record
Abstract
A photonic crystal (PhC) is a periodic structure with nanometric periodicity comparable with the wavelength of light, having a photonic band gap in the visible range: in practice, it reflects selectively only a band of the incident light, thus appearing to the observer of a determinate color. In this contribution, we propose to use photonic crystals as a colorimetric sensitive material for Structural Heath Monitoring. The idea is based on the observation that any distortion in the crystal structure produces a change in the reflected light bandwidth, resulting in turn in a change in its apparent color, visible to naked eyes. In a near future, we will be able to speed a photonic sensitive material on the surface of a structure in the form of a thin paint layer, and directly measure any variation in the strain field by simply observing change in color. To demonstrate this concept, we first we introduce the basic formulation that controls the photo-mechanical behavior of a 3D photonic structures. Next, we demonstrate the feasibility of the fabrication of a PhC made of sub-micrometric polystyrene colloidal spheres in a PDMS matrix on a rubber substrate. Through laboratory experiments, we show that the photonic properties of the crystal change with substrate elongation according to theoretical prediction. Lastly, we introduce a Finite Difference Time Domain (FDTD) analysis method to simulate the opto-mechanical response of a generic photonic crystal design, through direct integration Maxwell’s equations, and validated the method compering the numerical results to the experimental data
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".