Cellulose Nanocrystal–ZnO Nanohybrids for Controlling Photocatalytic Activity and UV Protection in Cosmetic Formulation
Bibliographic record
Abstract
A high-performance semiconductor zinc oxide (ZnO) on melamine formaldehyde-coated cellulose nanocrystals (MFCNCs) was synthesized and evaluated for its application in smart cosmetics. These ZnO@MFCNC hybrid nanostructures were evaluated for their in vitro sun protection factor performance and photocatalytic activity under simulated UV and solar radiation. The photodegradation kinetics of a model pigment (methylene blue) was fitted to the Langmuir-Hinshelwood model. A 4-fold increase in the photocatalytic activity of ZnO@MFCNCs was observed when compared to pure ZnO. This is associated with (i) increased specific surface area provided by the MFCNC template, (ii) confined surface energy and controlled growth of ZnO nanoparticles, and (iii) entrapment of photoinduced charge carriers in the pores of the core-shell MFCNC rod, followed by fast promotion of interfacial e-charge transfer to the surface of the catalyst. The present study demonstrates how an increase in photocatalytic activity can be engineered without the introduction of structural defects or band gap tailoring of the semiconductor. The aqueous-based ZnO@MFCNC hybrid system displayed attractive UV-absorption and photocatalytic characteristics, offering the conversion of this renewable and sustainable technology into intelligent cosmetic formulations.
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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) | 0.000 | 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".