Porous optical filter with spatially graded response
Bibliographic record
Abstract
Glancing Angle Deposition (GLAD) has become a common technique for producing thin porous films with various structural features. In the current work, we have further modified GLAD in order to create porous structures that have spatially graded profiles, suitable for application in fluidic and sensor devices. During the single-step GLAD process, macroscopic shadowing was used to produce a film with thickness variation across its length. Conventional GLAD substrate motion is used, but at the centre of the substrate holder a large shadowing cylinder is placed. During deposition, a shadow is cast on the substrate. To illustrate the usefulness of this shadowing property, a spatially graded optical rugate interference stop-band filter, with defect-induced transmission peak, was grown using the macroscopic GLAD shadowing method. The rugate filter produced had a graded refractive index periodicity across its length. This resulted in a transmittance spectrum and a transmittance peak location that changed across the length of the substrate. We have fabricated a porous filter with spatially graded response which allows impregnation of optically active materials, liquids, and gases in order to provide a sensor device with advanced or specialized capabilities. Spatially graded micro-fluidic or microchromatograph devices are also possible.
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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) | 0.000 | 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".