Cellulose Nanocrystal:Polymer Hybrid Optical Diffusers for Index‐Matching‐Free Light Management in Optoelectronic Devices
Bibliographic record
Abstract
Abstract A novel optical diffuser based on cellulose nanocrystals (CNCs) embedded in polydimethylsiloxane (PDMS) matrix, or cellulose nanocrystal:polymer (CNP) is reported. By offering simple low‐cost fabrication process as well as compatibility with large‐scale production, the proposed optical diffuser is a better choice for integration into optoelectronic devices for light management compared to other cellulose‐based diffusers due to its physical durability and the lack of requirement for index‐matching between the diffuser and the optoelectronic device thanks to the unique surface properties, mechanical flexibility, and optical transparency offered by PDMS as the matrix material. It is demonstrated that CNCs are an excellent filler material to pair up with PDMS in an optical diffuser providing highly efficient broadband light diffusion in visible and near‐infrared range of light at very low concentrations (≈1 wt%). At its optimized form, a CNP hybrid optical diffuser is capable of achieving very high haze values (up to 85%) while maintaining a high degree of transparency (≈85%) at the same time. As a proof of concept, light management capabilities of CNP hybrid optical diffusers are leveraged to demonstrate their potential for light extraction improvement in organic light‐emitting diodes and light absorption enhancement in thin‐film solar cells.
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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".