Durable Hydrophobic Iridescent Films with Tunable Colors from Self‐Assembled Cellulose Nanocrystals
Bibliographic record
Abstract
Cellulose nanocrystals (CNCs) are known to self-assemble into a left-handed chiral nematic lyotropic liquid crystalline phase in water. When captured in the solid state, this structure can impart films with photonic properties that make them promising candidates in photonics, sensing, security, and other areas. Unfortunately, the intrinsic hydrophilicity of CNCs renders these iridescent films susceptible to moisture, thereby limiting their practicality. To address this issue, a novel strategy to prepare hydrophobic iridescent films from pre-assembled CNC films is reported here. These films underwent a swelling process, followed by esterification using acid anhydrides to render them hydrophobic. By increasing the alkyl chain length of the anhydride reagent, the hydrophobicity of the resulting iridescent films can be enhanced. They showed water contact angles ranging from 34° to 115° and demonstrated tunable structural color spanning from blue to red. Moreover, they also exhibited good durability when exposed to water for 24 h. This innovative method for producing durable hydrophobic iridescent thin films is expected to facilitate their use in water-proof photonic coatings, optical sensors, and other applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".