Photoreversible Soft Azo Dye Materials: Toward Optical Control of Bio‐Interfaces
Bibliographic record
Abstract
Abstract Photoreversible optically switchable azo dye molecules in polymer‐based materials can be harnessed to control a wide range of physical, chemical, and mechanical material properties in response to light, that can be exploited for optical control over the bio‐interface. As a stimulus for reversibly influencing adjacent biological cells or tissue, light is an ideal triggering mechanism, since it can be highly localized (in time and space) for precise and dynamic control over a biosystem, and low‐power visible light is also an inherently gentle, benign, and nondamaging stimulus in a biological environment. Azobenzene‐based dyes in particular are emerging as especially attractive candidates among photoreversible molecules, and soft azobenzene‐containing materials are promising due to their ease of incorporation, and efficient and robust photochemistry and photophysics. This review provides a current survey of the use of photoreversible azo soft materials in cell biology and tissue engineering bio‐interface applications, to afford light control over molecular motion (orientation, flow), by inscribing surface morphological patterns or macroscopically photoactuating surfaces and structures, via three key photophysical and bioactive effects enabled by the azo groups' light‐induced photo‐orientation, topological optical patterning, and photomechanical actuation.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".