Humidity-Responsive Photonic Films and Coatings Based on Tuned Cellulose Nanocrystals/Glycerol/Polyethylene Glycol
Bibliographic record
Abstract
It has been extensively reported that cellulose nanocrystals (CNCs) can represent structural colors due to their unique chiral-nematic self-assembly. However, the application of this remarkable structure does need further investigation. It has been challenging to keep the selective reflection band (SRB) resulting from the CNC structure in the visible spectrum. Herein, composition of CNC colloidal suspensions with polyethylene glycol (PEG) and glycerol (Gly) have been studied to develop humidity-responsive sensors in the form of coatings and films. The fabricated samples were characterized for their mechanical properties, optical properties, water uptake capacity, water contact angle, and surface roughness. Additionally, the chemical structure of the samples was studied with FTIR spectroscopy. The produced humidity indicators on microbial glass slides were maintained and tested in a different relative humidity range from 20% to 98% with a different color response from blue to red, respectively. The color change of the humidity sensors was reversible for several cycles. It should be noted that the color change can be detected easily by the naked eye. The water uptake test showed that pure CNC and CNC/Gly had the lowest (34%) and highest (83%) water absorption levels. The mechanical tests for CNC/PEG composites showed the highest tensile strength (40.22 MPa). Moreover, microstructural characterizations confirmed the CNC pitch formation in all the samples. Addition of the fillers increased the CNC pitch, resulting in a mesoporous film formation. These produced humidity sensors are promising candidates in food and drug packaging due to their biodegradability, biocompatibility, and cost-effectiveness.
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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".