Novel elastomeric spiropyran-doped poly(dimethylsiloxane) optical waveguide for UV sensing
Bibliographic record
Abstract
Abstract Novel poly(dimethylsiloxane) (PDMS) doped with two different spiropyran derivatives (SP) were investigated as potential candidates for the preparation of elastomeric waveguides with UV-dependent optical properties. First, free-standing films were prepared and evaluated with respect to their photochromic response to UV irradiation. Kinetics, reversibility as well as photofatigue and refractive index of the SP-doped PDMS samples were assessed. Second, SP-doped PDMS waveguides were fabricated and tested as UV sensors by monitoring changes in the transmitted optical power of a visible laser (633 nm). UV sensing was successfully demonstrated by doping PDMS using one spiropyran derivative whose propagation loss was measured as 1.04 dB/cm at 633 nm, and sensitivity estimated at 115% change in transmitted optical power per unit change in UV dose. The decay and recovery time constants were measured at 42 and 107 s, respectively, with an average UV saturation dose of 0.4 J/cm 2 . The prepared waveguides exhibited a reversible and consistent response even under bending. The sensor parameters can be tailored by varying the waveguide length up to 21 cm, and are affected by white light and temperatures up to 70 ℃. This work is relevant to elastomeric optics, smart optical materials, and polymer optical waveguide sensors. Graphical Abstract
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".