Optically transparent nanocomposites reinforced with modified biocellulose nanofibers
Bibliographic record
Abstract
Abstract The objective of this study is to produce a class of optically transparent nanostructured biocomposites composed of surface‐modified bacterial cellulose (BC) nanofibers reinforced into poly(hydroxyethyl methacrylate) (PHEMA) hydrogel matrix. The surface of BC was first modified by fibrous heterogeneous acetylation to preserve the BC nanofibrillar morphology, followed by graft copolymerization with PHEMA hydrogel by free‐radical mechanisms using benzoyl‐peroxide as a radical initiator. A series of samples of grafted nanofiber having different degrees of acetylation and graft yields were produced and characterized using NMR, FTIR, and gravimetry. The maximum degree of acetylation obtained in this study was 2.3% and the maximum graft yield was 82.35 %.The modified nanofibers were thereafter reinforced into a polymeric matrix of PHEMA to form the final transparent biocomposite. The nanofiber‐network‐reinforced PHEMA polymer composite sample containing 1% (w/w) nanofiber transmitted over 80% of the light, while samples with less than 1% (w/w) nanofibrillar content exhibited higher light transmittances. The loss of transparency in the nanocomposite was small, despite the differences of refractive indices of BC and PHEMA. Increasing content of the BC nanofibers in the composite up to 1.4% (w/w) increased its water holding capacity up to 48.7% compared to the reference sample. This class of transparent nanostructured cellulose‐based hydrogel composite provides unique fluid handling capability of absorption and donation. These characteristics are essential for several applications as optically functional materials in addition to several biomedical applications. © 2012 Wiley Periodicals, Inc. J Appl Polym Sci, 2012
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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".