Bionanocomposite Films from Resilin-CBD Bound to Cellulose Nanocrystals
Bibliographic record
Abstract
This research explores the properties of bionanocomposite films prepared by binding recombinant resilin-like protein (res) consisting of the exon 1 resilin sequence from Drosophila melanogaster, engineered to include a cellulose binding domain (CBD), to cellulose nanocrystals (CNCs). The optimal binding of res-CBD to CNCs was 1:5 by mass, and the resulting res-CBD-CNCs remained colloidally stable in water. Res-CBD-CNCs were solventcast into transparent, free-standing films, which were more hydrophobic than neat CNC films, with water contact angles of 70–80° compared to 35–40° for the latter. In contrast to the multi-domain orientation typical of chiral nematic CNC films, res-CBD-CNC and CBD-CNC films exhibited long-range, uniaxial orientation that was apparently driven by the CBD moiety. Glycerol was studied as an additive in the films to determine whether the addition of a wet component to solvate the recombinant protein improved the mechanical properties of the res-CBD-CNC films. In comparison to the other films, res-CBD-CNC films were more elastic with added glycerol, in the range of 0.5–5 wt% (i.e., the films responded more elastically to a given strain and/or were less plastically deformed by a given mechanical load), but became less elastic at 25 wt% glycerol. Overall, films made of res-CBD-CNCs plus 0.5 wt% glycerol displayed improved mechanical properties compared to neat CNC films, with an increase in toughness of 150% and in elasticity of 100%.
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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".