Recent Advances in PDMS Optical Waveguides: Properties, Fabrication, and Applications
Bibliographic record
Abstract
Abstract Poly(dimethylsiloxane) (PDMS) has emerged as a promising polymer for fabricating optical waveguides. Its optical transparency, stretchability, flexibility, biocompatibility, and facile processing are a complement to common optical materials that are more brittle and stiff such as fused silica, polystyrene (PS), and poly(methyl methacrylate) (PMMA). Although PDMS is not a new material, with its first synthesis dating back to the early twentieth century, recent decades have seen an increased effort to expand its use in optical waveguides beyond conventional rubber applications. This review compiles established concepts and new advancements in PDMS science to shed light on limitations and new opportunities to better harness PDMS’ potential for optical waveguiding. With the materials science tetrahedron in mind (structure, properties, processing, and performance), this review explores the state‐of‐the‐art in PDMS waveguide technology and exposes relevant basic concepts pertaining to its physicochemical properties. The goal is to equip the photonics community with knowledge to further expand PDMS waveguide technology. The review covers three main topics: PDMS’ key properties (chemical, optical, thermal, and mechanical, besides biological and environmental aspects); PDMS waveguide fabrication techniques (processing, refractive index tuning, and post‐processing); and its applications. The review concludes with a discussion of current challenges and future prospects.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".