Hydrogel-Coated Polydimethylsiloxane with Reversible Transparency for Advanced Optical Switching
Bibliographic record
Abstract
Functional soft materials that swell in water often exhibit surface wrinkling, similar to the ridges formed on human skin after prolonged immersion, typically leading to reduced optical transmittance. Surprisingly, there is a scarcity of materials that are transparent underwater yet opaque in air, despite their vast potential in applications such as smart windows, periscopes, and information encryption. Herein, we report a hydrogel-based system comprising a polyacrylamide layer on polydimethylsiloxane (PDMS), demonstrating a reversible transition between opacity in air and high transparency in water or wet conditions. Upon water-induced swelling, the transmittance of the hydrogel layer markedly increases from 7.8% in air to 77.1% with excellent repeatability. This behavior enables applications such as optical encryption and decryption and water writing. Micro- and nanostructural analysis reveals that the optical switching arises from the reduction in local surface roughness upon hydrogel swelling. Furthermore, when employed as a smart window, the hydrogel layer effectively reduces solar power transmission by 36%, achieving a temperature reduction of 5.09 °C under direct sunlight while retaining heat in the absence of sunlight. These findings highlight the hydrogel layer on PDMS as a versatile platform for water-responsive smart devices, offering exciting opportunities in optical encryption, interactive writing systems, and energy-efficient window technologies.
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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) | 0.002 | 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".