Biohybrid Nanocellulose–Lysozyme Amyloid Aerogels via Electrostatic Complexation
Bibliographic record
Abstract
Modern science is increasingly turning to nature for inspiration to design sustainable biomaterials in a smart and effective way. Herein, we describe biohybrid aerogels based on electrostatic complexation between cellulose and proteins-two of the most abundant natural polymers on Earth. The effects of both particle surface charge and particle size are investigated with respect to aerogel properties including the morphology, surface area, stability, and mechanical strength. Specifically, negatively charged nanocellulose (cellulose nanocrystals and cellulose nanofibers) and positively charged lysozyme amyloid fibers (full-length and shortened via sonication) are investigated in the preparation of fibrillar aerogels, whereby the nanocellulose component was found to have the largest effect on the resulting aerogel properties. Although electrostatic interactions between these two classes of charged nanoparticles allow us to avoid the use of any cross-linking agents, the resulting aerogels demonstrate a simple additive performance as compared to their respective single-component aerogels. This lack of synergy indicates that although electrostatic complexation certainly leads to the formation of local aggregates, these interactions alone may not be strong enough to synergistically improve bulk aerogel properties. Nevertheless, the results reported herein represent a critical step toward a broader understanding of biohybrid materials based on cellulose and proteins.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 | 0.001 |
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 teacher head, 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".